人脸识别论文文献翻译中英文_大学论文
支持人脸识别的英语作文
支持人脸识别的英语作文英文回答：In our modern, tech-savvy world, face recognition technology has emerged as a transformative tool with immense potential. With its ability to accurately identify individuals, it promises to revolutionize various aspects of our lives. As someone who firmly believes in its benefits, I enthusiastically endorse the adoption of face recognition technology.From enhancing security measures to streamlining daily tasks, face recognition offers a plethora of advantages. In a world marred by threats and vulnerabilities, it plays a crucial role in safeguarding our physical and digital spaces. The technology enables swift and accurate identification of individuals, ensuring only authorized personnel gain access to sensitive areas or online platforms.Beyond security applications, face recognition also enhances convenience. Imagine walking into a store and having the system identify you, providing personalized recommendations based on your preferences. It eliminates the need for tedious password inputs or remembering multiple identification cards. In the healthcare sector, face recognition can expedite patient registration and improve treatment accuracy by accessing medical records instantly.Moreover, face recognition has the potential to foster inclusivity and bridge societal divides. By eliminating language barriers and accommodating individuals with disabilities, it ensures everyone has equal access to essential services. It empowers the visually impaired to navigate public spaces confidently and enables non-native speakers to communicate seamlessly.Additionally, face recognition technology can contribute to scientific research and innovation. It aids in identifying patterns and correlations within large datasets,leading to groundbreaking discoveries in fields such as medicine and genetics. By recognizing facial expressions and emotions, it can assist in understanding human behavior and developing therapies for mental health disorders.Of course, concerns regarding privacy and potential misuse must be addressed. However, with robust ethical frameworks and stringent regulations, we can harness the benefits of face recognition technology while safeguarding individual rights. It is essential to ensure responsible implementation and prevent unauthorized access to sensitive information.In conclusion, face recognition technology holds immense promise for transforming our lives. Its ability to enhance security, streamline tasks, foster inclusivity, and contribute to research makes it a valuable tool in our technological arsenal. By embracing its potential while addressing ethical considerations, we can unlock the transformative power of face recognition and shape a brighter future for all.中文回答：在我们这个科技发达的现代社会，人脸识别技术已经成为一种极具潜力的变革性工具。
人脸识别论文文献翻译中英文
人脸识别论文文献翻译中英文人脸识别论文中英文附录(原文及译文)翻译原文来自Thomas David Heseltine BSc. Hons. The University of YorkDepartment of Computer ScienceFor the Qualification of PhD. -- September 2005 -《Face Recognition: Two-Dimensional and Three-Dimensional Techniques》4 Two-dimensional Face Recognition4.1 Feature LocalizationBefore discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localisation. Depending on the application, if the position of the face within the image is known beforehand (for a cooperative subject in a door access system for example) then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localisation here, with a brief discussion of face detection in the literature review(section 3.1.1).The eye localisation method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented arerepresentative of the face recognition accuracy and not a product of the performance of the eye localisation routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation.We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of faces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template.Figure 4-1 - The average eyes. Used as a template for eye detection.Both eyes are included in a single template, rather thanindividually searching for each eye in turn, as the characteristic symmetry of the eyes either side of the nose, provides a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale(i.e. subject distance from thecamera) and also introduces the assumption that eyes in the image appear near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin just beneath the eyes. The reason being that in some cases the eyebrows can closely match the template, particularly if there are shadows in the eye-sockets, but the area of skin below the eyes helps to distinguish the eyes from eyebrows (the area just below the eyebrows contain eyes, whereas the area below the eyes contains only plain skin).A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the image with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smallertemplate of the individual left and right eyes then refines each eye position.This basic template-based method of eye localisation, although providing fairly preciselocalisations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.Eye localisation is performed on the set of training images, whichis then separated into two sets: those in which eye detection was successful; and those in which eye detection failed. Taking the set of successful localisations we compute the average distance from the eye template (Figure 4-2 top). Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as wewould expect. However, bright points do occur near the whites of the eye, suggesting that this area is often inconsistent, varying greatly fromthe average eye template.Figure 4-2 – Distance to the eye template for successful detections (top) indicating variance due tonoise and failed detections (bottom) showing credible variance dueto miss-detected features.In the lower image (Figure 4-2 bottom), we have taken the set of failed localisations(images of the forehead, nose, cheeks, background etc. falsely detected by the localisation routine) and once again computed the average distance from the eye template. The bright pupils surrounded by darker areas indicate that a failed match is often due to the high correlation of the nose and cheekbone regions overwhelming the poorly correlated pupils. Wanting to emphasise the2difference of the pupil regions for these failed matches and minimise the variance of the whites of the eyes for successful matches, we divide the lower image values by the upper image to produce a weights vector as shown in Figure 4-3. When applied to the difference image before summing a total error, this weighting scheme provides a much improved detection rate.Figure 4-3 - Eye template weights used to give higher priority to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begin our investigation into face recognition with perhaps the simplest approach,known as the direct correlation method (also referred to as template matching by Brunelli and Poggio [ 29 ]) involving the direct comparison of pixel intensity values taken from facial images. We use the term ‘Direct Correlation’ to encompass all techniques in which face images are compared directly, without any form of image spaceanalysis, weighting schemes or feature extraction, regardless of the distance metric used. Therefore, we do not infer that Pearson’s correlation is applied as the similarity function (although such an approach would obviously come under our definition of direct correlation). We typically use the Euclidean distance as our metric in these investigations (inversely related to Pearson’s correlation and can be considered as a scale and translation sensitive form of image correlation), as this persists with the contrast made between image space and subspace approaches in later sections.Firstly, all facial images must be aligned such that the eye centres are located at two specified pixel coordinates and the image cropped to remove any backgroundinformation. These images are stored as greyscale bitmaps of 65 by 82 pixels and prior to recognition converted into a vector of 5330 elements (each element containing the corresponding pixel intensity value). Each corresponding vector can be thought of as describing a point within a 5330 dimensional image space. This simple principle can easily be extended to much larger images: a 256 by 256 pixel image occupies a single point in 65,536-dimensional image space and again, similar images occupy close points within that space. Likewise, similar faces are located close together within the image space, while dissimilar faces are spaced far apart. Calculating the Euclidean distance d, between two facial image vectors (often referred to as thequery image q, and gallery image g), we get an indication of similarity. A threshold is thenapplied to make the final verification decision.d q g (d threshold ?accept d threshold ?reject ) . Equ. 4-134.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a person's most likely identity from a set of potential matches in a database. In order to assess a given system’s ability to perform these tasks, a variety of evaluation methodologies have arisen. Some of these analysis methods simulate a specific mode of operation (i.e. secure site access or surveillance), while others provide a more mathematical description of data distribution in someclassification space. In addition, the results generated from each analysis method may be presented in a variety of formats. Throughout the experimentations in this thesis, we primarily use the verification test as our method of analysis and comparison, although we also use Fisher’s Linear Discriminant to analyse individual subspace components in section 7 and the identification test for the final evaluations described in section 8. The verification test measures a system’s ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to two images being presented forcomparison, for which the system must return either an acceptance (the two images are of the same person) or rejection (the two images are of different people). The test is designed to simulate the application area of secure site access. In this scenario, a subject will present some form of identification at a point of entry, perhaps as a swipe card, proximity chip or PIN number. This number is then used to retrieve a stored image from a database of known subjects (often referred to as the target or gallery image) and compared with a live image captured at the point of entry (the query image). Access is then granted depending on the acceptance/rejection decision.The results of the test are calculated according to how many times the accept/reject decision is made correctly. In order to execute this test we must first define our test set of face images. Although the number of images in the test set does not affect the results produced (as the error rates are specified as percentages of image comparisons), it is important to ensure that the test set is sufficiently large such that statistical anomalies become insignificant (for example, a couple of badly aligned images matching well). Also, the type of images (high variation in lighting, partial occlusions etc.) will significantly alter the results of the test. Therefore, in order to compare multiple face recognition systems, they must be applied to the same test set.However, it should also be noted that if the results are to be representative of system performance in a real world situation, then the test data should be captured under precisely the same circumstances asin the application environment.On the other hand, if the purpose of the experimentation is to evaluate and improve a method of face recognition, which may be applied to a range of application environments, then the test data should present the range of difficulties that are to be overcome. This may mean including a greater percentage of ‘difficult’ images than4would be expected in the perceived operating conditions and hence higher error rates in the results produced. Below we provide the algorithm for executing the verification test. The algorithm is applied to a single test set of face images, using a single function call to the face recognition algorithm: CompareFaces(FaceA, FaceB). This call is used to compare two facial images, returning a distance score indicating how dissimilar the two face images are: the lower the score the more similar the two face images. Ideally, images of the same face should produce low scores, while images of different faces should produce high scores.Every image is compared with every other image, no image is compared with itself and no pair is compared more than once (we assume that the relationship is symmetrical). Once two images have been compared, producing a similarity score, the ground-truth is used to determine if the images are of the same person or different people. In practicaltests this information is often encapsulated as part of the image filename (by means of a unique person identifier). Scores are thenstored in one of two lists: a list containing scores produced by comparing images of different people and a list containing scores produced by comparing images of the same person. The finalacceptance/rejection decision is made by application of a threshold. Any incorrect decision is recorded as either a false acceptance or false rejection. The false rejection rate (FRR) is calculated as the percentage of scores from the same people that were classified as rejections. The false acceptance rate (FAR) is calculated as the percentage of scores from different people that were classified as acceptances.For IndexA = 0 to length(TestSet)For IndexB = IndexA+1 to length(TestSet)Score = CompareFaces(TestSet[IndexA], TestSet[IndexB])If IndexA and IndexB are the same personAppend Score to AcceptScoresListElseAppend Score to RejectScoresListFor Threshold = Minimum Score to Maximum Score:FalseAcceptCount, FalseRejectCount = 0For each Score in RejectScoresListIf Score <= ThresholdIncrease FalseAcceptCountFor each Score in AcceptScoresListIf Score > ThresholdIncrease FalseRejectCount5FalseAcceptRate = FalseAcceptCount / Length(AcceptScoresList)FalseRejectRate = FalseRejectCount / length(RejectScoresList)Add plot to error curve at (FalseRejectRate, FalseAcceptRate)These two error rates express the inadequacies of the system when operating at a specific threshold value. Ideally, both these figures should be zero, but in reality reducing either the FAR or FRR (by altering the threshold value) will inevitably result in increasing the other. Therefore, in order to describe the full operating range of a particular system, we vary the threshold value through the entire range of scores produced. The application of each threshold value produces an additional FAR, FRR pair, which when plotted on a graph produces the error rate curve shown below.6Figure 4-5 - Example Error Rate Curve produced by the verification test.The equal error rate (EER) can be seen as the point at which FAR is equal to FRR. This EER value is often used as a single figure representing the general recognition performance of a biometric system and allows for easy visual comparison of multiple methods. However, it is important to note that the EER does not indicate the level of error that would be expected in a real world application. It is unlikely that any real system would use a threshold value such that the percentage of false acceptances were equal to the percentage of false rejections. Secure site access systems would typically set the threshold such that false acceptances were significantly lower than false rejections: unwilling to tolerate intruders at the cost of inconvenient access denials. Surveillance systems on the other hand would require low false rejection rates to successfully identify people in a less controlled environment. Therefore we should bear in mind that a system with a lower EER might not necessarily be the better performer towards the extremes of its operating capability.There is a strong connection between the above graph and thereceiver operating characteristic (ROC) curves, also used in such experiments. Both graphs are simply two visualisations of the same results, in that the ROC format uses the True Acceptance Rate(TAR), where TAR = 1.0 – FRR in place of the FRR, effectively flipping thegraph vertically. Another visualisation of the verification test results is to display both the FRR and FAR as functions of the threshold value. This presentation format provides a reference to determine the threshold value necessary to achieve a specific FRR and FAR. The EER can be seen as the point where the two curves intersect.7Figure 4-6 - Example error rate curve as a function of the score thresholdThe fluctuation of these error curves due to noise and other errors is dependant on the number of face image comparisons made to generate the data. A small dataset that only allows for a small number of comparisons will results in a jagged curve, in which large steps correspond to the influence of a single image on a high proportion of thecomparisons made. A typical dataset of 720 images (as used insection 4.2.2) provides 258,840 verification operations, hence a drop of 1% EER represents an additional 2588 correct decisions, whereas the quality of a single image could cause the EER tofluctuate by up to 0.28.4.2.2 ResultsAs a simple experiment to test the direct correlation method, we apply the technique described above to a test set of 720 images of 60 different people, taken from the AR Face Database [ 39 ]. Every image is compared with every other image in the test set to produce a likeness score, providing 258,840 verification operations from which to calculate false acceptance rates and false rejection rates. The error curve produced is shown in Figure 4-7.Figure 4-7 - Error rate curve produced by the direct correlation method using no image preprocessing.We see that an EER of 25.1% is produced, meaning that at the EER threshold8approximately one quarter of all verification operations carried out resulted in an incorrect classification. There are a number of well-known reasons for this poor level of accuracy. Tiny changes in lighting, expression or head orientation cause the location in image space to change dramatically. Images in face space are moved far apart due to these image capture conditions, despite being of the same person’s face. The distance between images of different people becomes smaller than the area of face space covered by images of the same person and hence false acceptances and false rejections occur frequently. Other disadvantages include the large amount of storage necessary for holding many face images and the intensive processing required for each comparison, making this method unsuitable for applications applied to a large database. In section 4.3 we explore the eigenface method, which attempts to address some of these issues.4 二维人脸识别4.1 功能定位在讨论比较两个人脸图像，我们现在就简要介绍的方法一些在人脸特征的初步调整过程。
人脸识别技术的中英文论文
①现代的人脸识别，特指通过分析、比较人脸视觉特征信息进行身份鉴别的计算机技术。
具体而言，就是通过视频采集设备获取识别对象的面部图像，再利用核心的算法对其脸部的五官位置、脸型和角度进行计算分析，进而和自身数据库里已有的范本进行比对，最后判断出用户的真实身份。
这是一项高端的计算机图像处理技术。
②在全球范围内，人脸识别系统的研究始于20 世纪60 年代。
人脸识别的优势在于其自然性和不被被测个体察觉的特点。
所谓自然性，是指该识别方式同人类（甚至其他生物）进行个体识别时所利用的生物特征相同。
人脸识别就是通过观察比较人脸来区分和确认身份的。
不被察觉的特点会使该识别方法不令人反感，并且因为不容易引起人的注意而不易被欺骗。
相对于指纹识别而言，人脸识别还具有非接触式（非侵犯式）的特点，因此更加友好、自然，更易被人们接受。
③随着科技的发展，人脸识别技术的应用已经不是什么新鲜事了。
外文文献翻译成品：基于人脸识别的移动自动课堂考勤管理系统(中英文双语对照)
外文标题：Face Recognition-Based Mobile Automatic Classroom Attendance Management System外文作者：Refik Samet,Muhammed Tanriverdi文献出处: 2018 International Conference on Cyberworlds (如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文2937单词，20013字符(字符就是印刷符)，中文4819汉字。
Face Recognition-Based Mobile Automatic ClassroomAttendance Management System Abstract—Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.Keywords—face detection, face recognition, eigenfaces, fisherfaces, local binary pattern, attendance management system, mobile application, accuracyI.INTRODUCTIONMost educational institutions are concerned with st udents’ participation in courses since student participation in the classroom leads to effective learning and increases success rates [1]. Also, a high participation rate in the classroom is a motivating factor for teachers and contributes to a suitable environment for more willing and informative teaching [2]. The most common practice known to increase attendance in a course is taking attendance regularly. There are two common ways to create attendance data. Some teachers prefer to call names and put marks for absence or presence. Other teachers prefer to pass around a paper signing sheet. After gathering the attendance data via either of these two methods, teachers manually enter the data into the existing system. However, those non-technological methods are not efficient ways since they are time- consuming and prone to mistakes/fraud. The present paper aims to propose an attendance-taking process via the existing technological infrastructure with some improvements. A face recognition-based mobile automatic classroom attendance management system has been proposed with a face recognition infrastructure allowing the use of smart mobile devices. In this scope, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces, and Local Binary Pattern (LBP), has been developedfor face recognition. The proposed system includes three different applications for teachers, students, and parents to be installed on their smart phones to manage and perform a real-time polling process, data tracking, and reporting. The data is stored in a cloud server and accessible from everywhere at any time. Web services are a popular way of communication for online systems, and RESTful is an optimal example of web services for mobile online systems [3]. In the proposed system, RESTful web services were used for communication among teacher, student, and parent applications and the cloud server. Attendance results are stored in a database and accessible by the teacher, student and parent mobile applications.The paper is organised as follows. Section II provides a brief literature survey. SectionIII introduces the proposed system, and section IV follows by implementation and results. The last section gives the main conclusions.II.LITERATURE SURVEYFingerprint reading systems have high installation costs. Furthermore, only one student at a time can use a portable finger recognition device, which makes it atime-consuming process [4]. In the case of a fixed finger recognition device at the entrance of the classroom, attendance-taking should be done under the teacher's supervision so that students do not leave after the finger recognition, which makes the process time-consuming for both the teacher and the students. In case of RFID card reading systems, attendance-taking is available via the cards distributed to students [5]. In such systems, students may resort to fraudulent methods by reading their friends' cards. Also, if a student forgets his/her card, a non- true absence may be saved in the system. The disadvantage of the classroom scanning systems with Bluetooth or beacon methods is that each student must carry a device. Because the field limit of the Bluetooth Low Energy (BLE) system cannot be determined, students who are not inthe classroom at the moment but are within the Bluetooth area limits may appear to be present in the attendance system [6]. There are different methods of classroom attendance monitoring using face recognition technology. One of these is a camera placed at the classroom entrance and the students entering the classroom are registered into the system by face recognition [7]. However, in this system students’faces could be recognised, although students can leave the classroom afterwards, and errors can occur in the polling information. Another method is the observation carried out with a camera placed in the classroom and the classroom image taken during the course. In this case, the cameras used in the system need to be changed frequently to keep producing better quality images. Therefore, this system is not very useful andcan become costly. In addition to all the aforementioned disadvantages, the most common disadvantage is that all these methods need extra equipment. The proposed system has been developed to address these disadvantages. The main advantages of the proposed system are flexible usage, no equipment costs, no wasted time, and easy accessibility.III.PROPOSED SYSTEMA.Architecture of the Proposed SystemThe proposed system's architecture based on mobility and flexibility is shown inFig.1.Figure 1. System ArchitectureThe system consists of three layers: Application Layer, Communication Layer, and Server Layer.Application Layer: In the application layer, there are three mobile applications connected to the cloud server by web services. a) Teacher Application: The teacher is the head of the system, so he/she has the privilege to access all the data. By his/her smart mobile device, he/she can take a photo of students in a classroom at any time. After the taking the photograph, the teacher can use this photo to register attendance. For this aim, the photo is sent to the cloud server for face detection and recognition processing. The results are saved into a database together with all the reachable data. The teacher gets a response by the mobile application and can immediately see the results. The teacher can also create a student profile, add a photo of each student, and add or remove a student to/from their class rosters. He/she can as well create and delete courses. Each course has a unique six- character code. The teacher can share this code with his/her students so they can access their attendance results via the student application. The teacher can access to all data and results based on each student's recognized photo stamped with a date. Additionally, an email message with attendance data of a class in Excel format can be requested, while the analytics of the attendance results is provided in the application. b) Student Application: Students can sign in courses with the teacher's email address and the six-character course code. They can add their photos by taking a photo or a 3-second long video. In case of errors, their uploaded photos can be deleted. Students can only see limited results of the attendance-taking process related to their attendance. To protect personal privacy, the class photos and detected portrait photos of each student can be accessed only by the teacher. If students are not in the classroom when an attendance-check is performed, they are notified of the attendance-check. In case of errors (if a student is present, but not detected by the system), he/she can notify the teacher so he/she can fix the problem. c) Family Application: Parents can see their children's attendanceresults for each class. Additional children profiles can be added into the system. Each parent is added to the student's application with name, surname, and email address. When a student adds his/her parents, they are automatically able to see the attendance results. They are also notified when their child is not in the classroom.2) Communication Layer: RESTful web services are used to communicate betweenthe applications and server layers. Requests are sent by the POST method. Each request is sent with a unique ID of the authorised user of the session. Only the authorised users can access and respond the the data to which they have right to access. Due to its flexibility and fast performance, JSON is used as the data format for web services response [8]. With this abstract web service layer, the system can easily be used for a new item in the application layer, such as web pages or a new mobile operating system.3)Server Layer: The server layer is responsible for handling the requests and sending the results to the client. Face detection and recognition algorithms are performed in this layer and more than 30 different web services are created for handling different requests from mobile applications.B.Face DetectionAccurate and efficient face detection algorithms improve the accuracy level of theface recognition systems. If a face is not detected correctly, the system will fail its operation, stop processing, and restart. Knowledge-based, feature-based,template-based, and statistics-based methods are used for face detection [9]. Since the classroom photo is taken under the teacher's control, pose variations could be limited to a small range. Viola-Jones face detection method with Ada- boost training is shown as the best choice for real-time class attendance systems [9, 10]. In the most basic sense, the desired objects are firstly found and introduced according to a certain algorithm. Afterwards, they are scanned to find matches with similar shapes [11].C.Face RecognitionThere are two basic classifications of face recognition based on image intensity: feature-based and appearance-based [12]. Feature-based approaches try to represent (approximate) the object as compilations of different features, for example, eyes, nose, chin, etc. In contrast, the appearance-based models only use the appearance captured by different two-dimensional views of the object-of-interest. Feature-based techniques are more time-consuming than appearance-based techniques. The real-time attendance management system requires low computational process time. Therefore, three appearance-based face recognition techniques such as Eigenfaces, Fisherfaces and LBP are used in the tested system. Fisherfaces and eigenfaces techniques have a varying success rate, depending on different challenges, like pose variation, illumination, or facial expression [13]. According to several previous studies, face recognition using LBP method gives very good results regarding speed and discrimination performance as well as in different lighting conditions [14, 15]. Euclidean distance is calculated by finding similarities between images for face recognition. A filtering system based on Euclidean distances calculated by Eigenfaces, Fisherfaces and LBP has been developed for face recognition. According to the developed system, firstly, minimum Euclidean distances of LBP, Fisherfaces andEigenfaces algorithms are evaluated in defined order. If the Euclidean distance of LBP algorithm is less than 40; else if Euclidean distance of Fisherfaces algorithm is less than 250; else if Euclidean distance of Eigenfaces algorithm is less than 1500, recognized face is recorded as the right match. Secondly, if the calculated Euclidean distances by the three methods are greater than the minimum Euclidean distances, the second level Euclidean distances (40-50 (for LBP), 250-400 (for Fisherfaces), 1500- 1800 (for Eigenfaces)) are evaluated in the same way. If the second level conditions are also not met, the filter returns the wrong match. Thirdly, if any two algorithms give the same match result, the match is recorded correctly. Finally, if no conditions are met, the priority is given to the LBP algorithm and the match is recorded correctly. The system’s specific architecture aimed for flexibility, mobility, and low-cost by requiring no extra equipment. At the same time, its objective was to provide access to all users at any time. The system thus offers a real-time attendance management system to all its users.IV.IMPLEMENTATION AND RESULTSThe following platform was used. The cloud server has a 2.5 GHz with 4-core CPU,8GB RAM, and 64-bit operating system capacity. Viola-Jones face detection algorithm and Eigenfaces, Fisherfaces and LBP face recognition algorithms were implemented based on OpenCV. Tests were done with both iOS and ANDROID.Forty different attendance monitoring tests were performed in a real classroom, including 11 students, and 264 students’ faces were detected. Tables I, II, and III show detection and recognition accuracy of all three different types of tested algorithms related to the Euclidean distance.Priority ordering for 3 algorithms was arranged according to accuracy rate for each interval. In test results, 123, 89, and 85 false recognitions were detected for Eigenfaces, Fisherfaces and LBP, respectively. By the help of the developed filtering system, the number of false recognitions decreased to 65. Out of 40 implemented attendance monitoring tests, 10 were conducted with 1 face photo of each student in database in Step-I, 20 were conducted when the number of face photos increased up to 3 in Step-II, and 10 recognition processes were conducted with more than 3 face photos in database in Step-III. Table IV shows the obtained results.The most important limitation of tested attendance monitoring process is decreased success with increasing distance between the camera and students. The results regarding students sitting in front seats are more accurate in comparison to results regarding students sitting in the back. Secondly, the accuracy rates may have decreased due to the blurring caused by vibration while the photo was taken. Thirdly, in some cases one part of the student's face may be covered by another student sitting in front of him/her, which may hamper a successful face recognition process. Since the classroom photos are taken in uncontrolled environments, the illumination and pose could, to a large extent, affect the accuracy rate. The developed filtering system minimizes these effects. To increase accuracy, pose tolerant face recognition approach may also be used [16, 17].V.CONCLUSIONSThe present paper proposes a flexible and real-time face recognition-based mobile attendance management system. A filtering system based on Euclidean distances calculated by Eigenfaces, Fisherfaces, and LBP has been developed. The proposed system eliminates the cost for extra equipment, minimizes attendance-taking time, and allows users to access the data anytime and anywhere. Smart devices are very user- friendly to perform classroom attendance monitoring. Teachers, students, and parents can use the application without any restrictions and in real-time. Since the internet connection speed has been steadily increasing, high quality, larger images can be sent to the server. In addition, processor capacity of the servers is also increasing on daily basis. With these technological developments, the accuracy rate of the proposed system will also be increased. Face recognition could be further tested by other face recognition techniques, such as Support Vector Machine, Hidden Markov Model, Neural Networks, etc. Additionally, detection and recognition processes could be performed on smart devices once their processor capacity is sufficiently increased. REFERENCES[1]L. Stanca, "The Effects of Attendance on Academic Performance:Panel Data Evidence for Introductory Microeconomics," J. Econ. Educ., vol. 37, no. 3, pp. 251–266, 2006.[2]P.K. Pani and P. Kishore, "Absenteeism and performance in a quantitative moduleA quantile regression analysis," Journal of Applied Research in Higher Education, vol.8 no. 3, pp. 376-389, 2016.[3]U. Thakar, A. Tiwari, and S. Varma, "On Composition of SOAP Based and RESTful Services," IEEE 6th Int. Conference on Advanced Computing (IACC), 2016. [4]K.P.M. Basheer and C.V. Raghu, "Fingerprint attendance system for classroom needs," Annual IEEE India Conference (INDICON), pp. 433-438, 2012.[5]S. Konatham, B.S. Chalasani, N. Kulkarni, and T.E. Taeib, ―Attendance generating system using RFID and GSM,‖IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2016.[6]S. Noguchi, M. Niibori, E. Zhou, and M. Kamada, "Student Attendance Management System with Bluetooth Low Energy Beacon and Android Devices," 18th International Conference on Network- Based Information Systems, pp. 710-713, 2015.[7]S. Chintalapati and M.V. Raghunadh, ―Automated attendance management system based on face recognition algorithms,‖IEEE Int. Conference on Computational Intelligence and Computing Research, 2013.[8]G. Wang, "Improving Data Transmission in Web Applications via the Translation between XML and JSON," Third Int. Conference on Communications and Mobile Computing (CMC), pp. 182-185, 2011.[9]X. Zhu, D. Ren, Z. Jing, L. Yan, and S. Lei, "Comparative Research of theCommon Face Detection Methods," 2nd International Conference on Computer Science and Network Technology, pp. 1528-1533, 2012.[10]V. Gupta and D. Sharma, ―A Study of Various Face Detection Methods,‖International Journal of Advanced Research in Computer and Communication Engineering vol. 3, no. 5, pp.6694-6697, 2014.[11]P. Viaola and M.J. Jones, ―Robust Real-Time Face Detection,‖International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.[12]L. Masupha, T. Zuva, S. Ngwira, and O. Esan, ―Face recognition techniques, their advantages, disadvantages and performance evaluation,‖Int. Conference on Computing, Communication and Security (ICCCS), 2015.[13]J. Li, S. Zhou, and C. Shekhar, "A comparison of subspace analysis for face recognition," International Conference on Multimedia and Expo, ICME '03, Proceedings, vol. 3, pp. 121-124, 2003.[14]T. Ahonen, A. Hadid, and M. Pietikainen, ―Face description with Local Binary Patterns,‖IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.[15]T. Ahonen, A. Hadid, M. Pietikainen, and T . Maenpaa, ―Face recognition based on the appearance of local regions,‖Proceedings of the 17th Int. Conference on Pattern Recognition, vol. 3, pp. 153-156, 2004.[16]R. Samet, S. Sakhi, and K. B. Baskurt, ―An Efficient Pose Tolerant Face Recognition Approach‖, Transactions on Comput. Science XXVI, LNCS 9550, pp.161-172, 2016.[17]R. Samet, G. S. Shokouh, J. Li, ―A Novel Pose Tolerant Face Recognition Approach‖, 2014 International Conference on Cyberworlds, pp. 308-312, 2014.基于人脸识别的移动自动课堂考勤管理系统摘要- 课堂出勤检查是学生参与和课程最终成功的一个因素。
人脸识别英文作文
人脸识别英文作文英文：Face recognition technology has become increasingly popular in recent years, with applications ranging from unlocking smartphones to identifying criminals in surveillance footage. As for me, I have personally experienced the convenience and benefits of face recognition technology in various aspects of my life.One of the most common uses of face recognition technology is in unlocking smartphones. I remember the days when I had to type in a passcode or use my fingerprint to unlock my phone. However, with the introduction of face recognition, I can now simply look at my phone and it unlocks automatically. This not only saves me time, but also adds an extra layer of security to my device.Another example of how face recognition technology has impacted my life is in airport security. When I traveled toChina last year, I was amazed at how quickly andefficiently I was able to go through the security checkpoints. Instead of having to show my passport and boarding pass multiple times, I simply had to stand infront of a camera for a few seconds and the system recognized my face, allowing me to proceed without any hassle.Furthermore, face recognition technology has also been used in law enforcement to identify suspects and solve crimes. Just last month, there was a case in my neighborhood where a thief was caught on a surveillance camera stealing from a local store. Thanks to the clear footage and the accuracy of face recognition technology, the thief was identified and apprehended within a matter of days.In addition to these practical examples, face recognition technology has also sparked debates about privacy and security. Some people are concerned about the potential misuse of facial data and the implications for personal privacy. However, I believe that as long as thetechnology is used responsibly and with proper regulations in place, the benefits far outweigh the risks.In conclusion, face recognition technology has undoubtedly made a significant impact on my daily life, from simplifying my smartphone usage to improving security measures in public spaces. While there are valid concerns about privacy and security, I am optimistic about the potential of this technology to continue improving and enhancing our lives in the future.中文：人脸识别技术近年来变得越来越流行，应用范围从解锁智能手机到在监控录像中识别罪犯。
中外文文献-基于pca的人脸识别
外文资料原文An Improved Hybrid Face Recognition Based on PCA andSubpattern TechniqueA.R Kulkarni, D.S BormaneAbstract: In this paper a new technique for face recognition Based on PCA is implement ed .Subpattern PCA(SpPCA) Is actually an improvement over PCA. It was found to give Better results so in this paper Integration of Different SpPCA methods with PCA was do ne and found to get Improvement in recognition Accuracy.Keyword s: Principle Component Analysis (PCA, Subpattern PCA(SpPCA), SpPCA I, SpP CAIII. INTRODUCTIONHumans have been using physical characteristics such as face, voice, gait, etc. to reco gnize each other for thousands of years. With new advances in technology, biometrics has become an emerging technology for recognizing individuals using their biological traits. Now, biometrics is becoming part of day to day life, where in a person is recognized by his/her personal biological characteristics. Examples of different Biometric systems include Fingerprint recognition, Face recognition, Iris recognition, Retina recognition, Hand geometr y, V oice recognition, Signature recognition, among others. Face recognition, in particular ha s received a considerable attention in recent years both from the industry and the research community.A) Facial Expression RecognitionRecognition of facial expression is important in human computer interaction, human robot interaction, digital entertainments, games, smart user interface for cellular phones and game s. Recognition of facial expression by using computer is a topic that has become under c onsideration not more than a decade. Facial expression in human is a reaction to analeptic s. For example reaction to a funny movie is laughter, laughing changes the figure of the face and state of the face muscles. By tracing these states changing and comparing them with the neutral face, facial expression can be recognized. Primary facial expressions whic h are anger, disgust, fear, happiness, sadness and surprise. Figure 1 illustrates these states of expressions. Implementing real time facial expression recognition is difficult and does n ot have impressive results because of person, camera, and illumination variations complicat e the distribution of the facial expressions. In this paper facial expressions are recognized by using still images.Manuscript received May, 2013Er .A. R. Kulkarni, received her Bachelor of Engg. Degree from W.C.E Sangli, Shivaji University, Maharashtra, India.Prof .Dr D.S Bormane , is the Director for JSPM’s Rajarshi Shahu College Of Engg, p une India.Fig.1 illustrates 6 states of facial ExpressionsHumans have been using physical characteristics such as face, voice, gait, etc. to recognize each other for thousands of years. With new advances in technology, biometrics has become an emerging technology for recognizing individuals using their biological traits. Now, biometrics is becoming part of day to day life, where in a person is recognized by his/her personal biological characteristics. Examples of different Biometric systems include Fingerprint recognition, Face recognition, Iris recognition, Retina recognition, Hand geometry, V oice recognition, Signature recognition, among others. Face recognition, in particular has received a considerable attention in recent years both from the industry and the research community. PCA, SpPCA are the feature extraction methods which have been used in this report in order to recognize the facial expressions. Each of the mentioned method shows different performance in terms of recognizing the expressions. The objective of our project is to create a matlab code that can be used to identify people using their face images.An Improved Hybrid Face Recognition Based on PCA and Subpattern TechniqueThis papert gives a brief background about biometrics. A particular attention is given to face recognition. Face recognition refers to an automated or semi-automated process of matching facial images. Many techniques are available to apply face recognition of which is Principle Component Analysis (PCA). PCA is a way of identifying patterns in data and expressing the data in such a way to highlight their similarities and differences. Before applying this method to face recognition, a brief introduction is given for PCA. SpPCAI & SpPCAII has also been applied. The Matlab code for a Hybrid Algorithm has been designed which consists integration of SpPCAI with SpPCAII. Thetraditional PCA [1] is a very effective approach of extracting features and has successfully been applied in pattern recognition such as face classification [2]. It operates directly on whole patterns represented as (feature) vectors to extract so-needed global features for subsequent classification by a set of previously found global projectors from a given training pattern set, whose aim is to maximally preserve original pattern information after extracting features, i.e., reducing dimensionality. In this paper, we develop another PCA operating directly on sub patterns rather than on whole pattern These sub patterns are formed via a partition for an original whole pattern and utilized to compose multiple training Subpattern sets for the original training pattern set. In this way, SpPCA can independently be performed on individual training subpattern sets and finds corresponding local projection sub-V ectors, and then uses them to extract local sub-features from any given pattern. Afterwards, these extracted sub-features from individual subpatterns are synthesized into a global feature of the original whole pattern for subsequent classification.II. PRINCIPAL COMPONENT ANALYSIS (PCA)The purpose of PCA is to reduce the dimensionality of data sets without losing significant information PCA is reducing the dimensionality of data set by performing covariance analysis between multidimensional data sets [31, 32]. Because PCA is classical technique that can do something in linear domain, applications that have linear models are suitable, for image processing. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. This can be called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of facial images (vectors)Fig.2 Original cropped image and image with 4 non-overlapped subpatternsII. PROPOSED SPPCAFig.3 Flowchart for Subpattern techniqueSpPCA includes two steps. In the first step, an original whole pattern denoted by a vector is partitioned into a set of equally-sized subpatterns in non-overlapping ways and then all those subpatterns sharing the same original feature components are respectively collected from the training set to compose Corresponding training subpattern setsas shown in “Fig1”. Secondly, PCA is performed on each of such subpattern sets. More specifically, we are given a set of training patterns X={X1 ,X2 , …XN, } with each column vector Xi for (i=1, 2, …, N) having m dimensions. Now according to the first step, an original whole pattern is firstpartitioned into K d- dimensional subpatterns in a non overlapping way and reshaped into a d-by-K matrix Xi=(Xi1,Xi2……Xik) , with Xij being the jth subpattern of Xi and i=1,2,…, N and j=1,2,…,K. And then according to the second step, we construct PCA for the jth subpattern set SPj= Xij,i=1,2….,N ) to seek its projection vectors Φj =(¢j1,¢j2,..¢jl) Here it is easy toprove that all total subscatter matrices are positive semi-definite and their scales are all dxd. And then find independently each set of projection sub-vectors by means of the following eigen value-eigenvector system under the constraints. After obtaining all individual projection sub-vectors from the partitioned subpattern sets, we can extract corresponding sub-features from any subpattern of a given whole pattern Then synthesize them into a global feature . Now on the basis of the synthesized global features, we can use the nearest neighbor (NN) rule [3] to perform pattern classification.Fig4 . Block Diagram for Hybrid ImplementationA) Algorithm for proposed Hybrid SchemeStep 1: Create database from input imageStep 2: Read Train and Test imageStep 3: Perform SpPCA I and SpPCA IIStep 4: Calculate The Recognition RatesStep5.Calculatetheoveralldistance Computation and classification.Step 6. Calculate the Recognition rate for Hybrid methodIII. EXPERIMENTAL RESULTSIn this paper, experiments are based on ORL face database, which can be used freely for academic research [7]. ORL face database contains 40 distinct persons, each person having ten different face images. There are 400 face images in total, with 256 gray degrees and the resolution of 92112×. These face images are attained in different situations, such as different time, different angles, different expression (closed eyes/open eyes, smile/surprise/angry/happy etc.) and different face details (glasses/no glasses, beard/no beard, different hair style etc.). Some images are shown inFig.2.A) Graphical Result Of Algorithms ImplementedFig7. PCAFig 8 . SpPCA IFig.9 SpPCAIIFig10.hybridFig11.Overall Recognition accuracyB) T able.1 Recognition Accuracy ComparisionC) Algorithm Comparision ChartFig.12 Recognition RateIV. CONCLUSIONA Hybrid Approach for face Recognition using Subpattern Technique is implemented. In this paper facial expression recognition using PCA, SpPCA approaches were done and compared...The results of experiments demonstrate SpPCA overcome PCA.. Therefore integration of SpPCA with PCA was done and found recognition accuracy to be improved. We can therefore say that our novel hybrid approach is robust and competitive.V. FUTURE SCOPEFace recognition has recently become a very active and interesting research area. Vigorous research has been conducted in this area for the past four decades and huge progress w ith encouraging results has been obtained. The goal of this paper is to provide a survey of recentholistic and feature based approaches that complement previous surveys. Current face recognition systems have already reached a certain level of maturity when operating under constrained conditions. However, we are still far from achieving the ideal and adequate results in all the various situations. Still more advances need to be done in the technology regarding the sensitivity of the face images to environmental conditions like illumination, occlusion, time-delays, pose orientations, facial expressions. Furthermore, research work on 3 D face recognition and face recognition in videos is also pacing parallel. However, the error rates of current face recognition systems are still too high for many of the applications. So, the researchers still need to go far to get accurate face recognitions.REFERENCES[1] T. Y oung, K-S Fu, Handbook of pattern recognition and image processing, Academic Press, 1986.[2] M. Turk, A. Pentland, Eigenfaces for recognition, Journal Cognitive Neuroscience, 3(1) (1991)71-86.[3] G. Loizou, S.J. Maybank, The nearest neighbor and the Bayes error rates, IEEE Trans. Patt. Anal. & Mach. Intell., vol.9 (1987)254-262,.[4] Seema Asht, Rajeshwar Dass, Dharmendar, “Pattern Recognition Techniques”, International Journal of Science, Engineering and Computer Technology, vol. 2, pp. 87-88, March 2012. [4] M. Kirby, L. Sirovich, “Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, No. 1, pp. 103-108, January 1990.[5] M. Turk and A. Pentland. “Face recognition using eigenfaces”. In Proceedings of the IEEE Conference on Computer V ision and Pattern Recognition, 1991[6] Face Recognition : National Science and Technology Council (NSTC) , Committee on Technology, Committee on Homeland and National Security, Subcommittee on biometrics.[7] N. Sun, H. Wang, Z. Ji, C. Zou, and L. Zhao, "An efficient algorithm for Kernel two-dimensional principal component analysis," Neural Computing & Applications, vol.17, pp.59-64, 2008.[8] Q. Y ang aand X. Q. Ding, "Symmetrical Principal Component Analysis and Its Application in Face Recognition," Chinese Journal of Computers, vol.26, pp.1146–1151, 2003.[9] J. Y ang and D. Zhang, "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, pp.131- 137, 2004.[10] K. R. Tan and S. C. Chen, "Adaptively weighted subpattern PCA for face recognition," Neurocomputing, vol.64, pp.505-511, 2005.外文资料译文一种基于PCA和子模式改进的混合人脸识别技术A.R Kulkarni, D.S Bormane摘要: 本文基于PCA人脸识别新技术实现的。
人脸识别技术外文翻译文献编辑
文献信息文献标题：Face Recognition Techniques: A Survey（人脸识别技术综述）文献作者：V.Vijayakumari文献出处：《World Journal of Computer Application and Technology》, 2013,1(2):41-50字数统计：英文3186单词，17705字符；中文5317汉字外文文献Face Recognition Techniques: A Survey Abstract Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.Keywords: Face Recognition, Illumination, Authentication, Security1.IntroductionDeveloped in the 1960s, the first semi-automated system for face recognition required the administrator to locate features ( such as eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. In the 1970s, Goldstein, Armon, and Lesk used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solutions was that the measurements and locations were manually computed. The face recognition problem can be divided into two main stages: face verification (or authentication), and face identification (or recognition).The detection stage is the first stage; it includesidentifying and locating a face in an image. The recognition stage is the second stage; it includes feature extraction, where important information for the discrimination is saved and the matching where the recognition result is given aid of a face database.2.Methods2.1.Geometric Feature Based MethodsThe geometric feature based approaches are the earliest approaches to face recognition and detection. In these systems, the significant facial features are detected and the distances among them as well as other geometric characteristic are combined in a feature vector that is used to represent the face. To recognize a face, first the feature vector of the test image and of the image in the database is obtained. Second, a similarity measure between these vectors, most often a minimum distance criterion, is used to determine the identity of the face. As pointed out by Brunelli and Poggio, the template based approaches will outperform the early geometric feature based approaches.2.2.Template Based MethodsThe template based approaches represent the most popular technique used to recognize and detect faces. Unlike the geometric feature based approaches, the template based approaches use a feature vector that represent the entire face template rather than the most significant facial features.2.3.Correlation Based MethodsCorrelation based methods for face detection are based on the computation of the normalized cross correlation coefficient Cn. The first step in these methods is to determine the location of the significant facial features such as eyes, nose or mouth. The importance of robust facial feature detection for both detection and recognition has resulted in the development of a variety of different facial feature detection algorithms. The facial feature detection method proposed by Brunelli and Poggio uses a set of templates to detect the position of the eyes in an image, by looking for the maximum absolute values of the normalized correlation coefficient of these templates at each point in test image. To cope with scale variations, a set of templates atdifferent scales was used.The problems associated with the scale variations can be significantly reduced by using hierarchical correlation. For face recognition, the templates corresponding to the significant facial feature of the test images are compared in turn with the corresponding templates of all of the images in the database, returning a vector of matching scores computed through normalized cross correlation. The similarity scores of different features are integrated to obtain a global score that is used for recognition. Other similar method that use correlation or higher order statistics revealed the accuracy of these methods but also their complexity.Beymer extended the correlation based on the approach to a view based approach for recognizing faces under varying orientation, including rotations with respect to the axis perpendicular to the image plane(rotations in image depth). To handle rotations out of the image plane, templates from different views were used. After the pose is determined ,the task of recognition is reduced to the classical correlation method in which the facial feature templates are matched to the corresponding templates of the appropriate view based models using the cross correlation coefficient. However this approach is highly computational expensive, and it is sensitive to lighting conditions.2.4.Matching Pursuit Based MethodsPhilips introduced a template based face detection and recognition system that uses a matching pursuit filter to obtain the face vector. The matching pursuit algorithm applied to an image iteratively selects from a dictionary of basis functions the best decomposition of the image by minimizing the residue of the image in all iterations. The algorithm describes by Philips constructs the best decomposition of a set of images by iteratively optimizing a cost function, which is determined from the residues of the individual images. The dictionary of basis functions used by the author consists of two dimensional wavelets, which gives a better image representation than the PCA (Principal Component Analysis) and LDA(Linear Discriminant Analysis) based techniques where the images were stored as vectors. For recognition the cost function is a measure of distances between faces and is maximized at each iteration. For detection the goal is to find a filter that clusters together in similar templates (themean for example), and minimized in each iteration. The feature represents the average value of the projection of the templates on the selected basis.2.5.Singular Value Decomposition Based MethodsThe face recognition method in this section use the general result stated by the singular value decomposition theorem. Z.Hong revealed the importance of using Singular Value Decomposition Method (SVD) for human face recognition by providing several important properties of the singular values (SV) vector which include: the stability of the SV vector to small perturbations caused by stochastic variation in the intensity image, the proportional variation of the SV vector with the pixel intensities, the variances of the SV feature vector to rotation, translation and mirror transformation. The above properties of the SV vector provide the theoretical basis for using singular values as image features. In addition, it has been shown that compressing the original SV vector into the low dimensional space by means of various mathematical transforms leads to the higher recognition performance. Among the various dimensionality reducing transformations, the Linear Discriminant Transform is the most popular one.2.6.The Dynamic Link Matching MethodsThe above template based matching methods use an Euclidean distance to identify a face in a gallery or to detect a face from a background. A more flexible distance measure that accounts for common facial transformations is the dynamic link introduced by Lades et al. In this approach , a rectangular grid is centered all faces in the gallery. The feature vector is calculated based on Gabor type wavelets, computed at all points of the grid. A new face is identified if the cost function, which is a weighted sum of two terms, is minimized. The first term in the cost function is small when the distance between feature vectors is small and the second term is small when the relative distance between the grid points in the test and the gallery image is preserved. It is the second term of this cost function that gives the “elasticity” of this matching measure. While the grid of the image remains rectangular, the grid that is “best fit” over the test image is stretched. Under certain constraints, until the minimum of the cost function is achieved. The minimum value of the cost function isused further to identify the unknown face.2.7.Illumination Invariant Processing MethodsThe problem of determining functions of an image of an object that are insensitive to illumination changes are considered. An object with Lambertian reflection has no discriminative functions that are invariant to illumination. This result leads the author to adopt a probabilistic approach in which they analytically determine a probability distribution for the image gradient as a function of the surfaces geometry and reflectance. Their distribution reveals that the direction of the image gradient is insensitive to changes in illumination direction. Verify this empirically by constructing a distribution for the image gradient from more than twenty million samples of gradients in a database of thousand two hundred and eighty images of twenty inanimate objects taken under varying lighting conditions. Using this distribution, they develop an illumination insensitive measure of image comparison and test it on the problem of face recognition. In another method, they consider only the set of images of an object under variable illumination, including multiple, extended light sources, shadows, and color. They prove that the set of n-pixel monochrome images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in IR and that the dimension of this illumination cone equals the number of distinct surface normal. Furthermore, the illumination cone can be constructed from as few as three images. In addition, the set of n-pixel images of an object of any shape and with a more general reflectance function, seen under all possible illumination conditions, still forms a convex cone in IRn. These results immediately suggest certain approaches to object recognition. Throughout, they present results demonstrating the illumination cone representation.2.8.Support Vector Machine ApproachFace recognition is a K class problem, where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier, they developed a SVM-based face recognition algorithm. The facerecognition problem is formulated as a problem in difference space, which models dissimilarities between two facial images. In difference space we formulate face recognition as a two class problem. The classes are: dissimilarities between faces of the same person, and dissimilarities between faces of different people. By modifying the interpretation of the decision surface generated by SVM, we generated a similarity metric between faces that are learned from examples of differences between faces. The SVM-based algorithm is compared with a principal component analysis (PCA) based algorithm on a difficult set of images from the FERET database. Performance was measured for both verification and identification scenarios. The identification performance for SVM is 77-78% versus 54% for PCA. For verification, the equal error rate is 7% for SVM and 13% for PCA.2.9.Karhunen- Loeve Expansion Based Methods2.9.1.Eigen Face ApproachIn this approach, face recognition problem is treated as an intrinsically two dimensional recognition problem. The system works by projecting face images which represents the significant variations among known faces. This significant feature is characterized as the Eigen faces. They are actually the eigenvectors. Their goal is to develop a computational model of face recognition that is fact, reasonably simple and accurate in constrained environment. Eigen face approach is motivated by the information theory.2.9.2.Recognition Using Eigen FeaturesWhile the classical eigenface method uses the KLT (Karhunen- Loeve Transform) coefficients of the template corresponding to the whole face image, the author Pentland et.al. introduce a face detection and recognition system that uses the KLT coefficients of the templates corresponding to the significant facial features like eyes, nose and mouth. For each of the facial features, a feature space is built by selecting the most significant “eigenfeatures”, which are the eigenvectors corresponding to the largest eigen values of the features correlation matrix. The significant facial features were detected using the distance from the feature space and selecting the closest match. The scores of similarity between the templates of the test image and thetemplates of the images in the training set were integrated in a cumulative score that measures the distance between the test image and the training images. The method was extended to the detection of features under different viewing geometries by using either a view-based Eigen space or a parametric eigenspace.2.10.Feature Based Methods2.10.1.Kernel Direct Discriminant Analysis AlgorithmThe kernel machine-based Discriminant analysis method deals with the nonlinearity of the face patterns’ distribution. This method also effectively solves the so-called “small sample size” (SSS) problem, which exists in most Face Recognition tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the Generalized Discriminant Analysis (GDA), respectively.2.10.2.Features Extracted from Walshlet PyramidA novel Walshlet Pyramid based face recognition technique used the image feature set extracted from Walshlets applied on the image at various levels of decomposition. Here the image features are extracted by applying Walshlet Pyramid on gray plane (average of red, green and blue. The proposed technique is tested on two image databases having 100 images each. The results show that Walshlet level-4 outperforms other Walshlets and Walsh Transform, because the higher level Walshlets are giving very coarse color-texture features while the lower level Walshlets are representing very fine color-texture features which are less useful to differentiate the images in face recognition.2.10.3.Hybrid Color and Frequency Features ApproachThis correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model(EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQcolor space, which combines the component image R of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.2.10.4.Multilevel Block Truncation Coding ApproachIn Multilevel Block Truncation coding for face recognition uses all four levels of Multilevel Block Truncation Coding for feature vector extraction resulting into four variations of proposed face recognition technique. The experimentation has been conducted on two different face databases. The first one is Face Database which has 1000 face images and the second one is “Our Own Database” which has 1600 face images. To measure the performance of the algorithm the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) parameters have been used. The experimental results have shown that the outcome of BTC (Block truncation Coding) Level 4 is better as compared to the other BTC levels in terms of accuracy, at the cost of increased feature vector size.2.11.Neural Network Based AlgorithmsTemplates have been also used as input to Neural Network (NN) based systems. Lawrence et.al proposed a hybrid neural network approach that combines local image sampling, A self organizing map (SOM) and a convolutional neural network. The SOP provides a set of features that represents a more compact and robust representation of the image samples. These features are then fed into the convolutional neural network. This architecture provides partial invariance to translation, rotation, scale and facedeformation. Along with this the author introduced an efficient probabilistic decision based neural network (PDBNN) for face detection and recognition. The feature vector used consists of intensity and edge values obtained from the facial region of the down sampled image in the training set. The facial region contains the eyes and nose, but excludes the hair and mouth. Two PDBNN were trained with these feature vectors and used one for the face detection and other for the face recognition.2.12.Model Based Methods2.12.1.Hidden Markov Model Based ApproachIn this approach, the author utilizes the face that the most significant facial features of a frontal face which includes hair, forehead, eyes, nose and mouth which occur in a natural order from top to bottom even if the image undergo small variation/rotation in the image plane perpendicular to the image plane. One dimensional HMM (Hidden Markov Model) is used for modeling the image, where the observation vectors are obtained from DCT or KLT coefficients. They given c face images for each subject of the training set, the goal of the training set is to optimize the parameters of the Hidden Markov Model to best describe the observations in the sense of maximizing the probability of the observations given in the model. Recognition is carried out by matching the best test image against each of the trained models. To do this, the image is converted to an observation sequence and then model likelihoods are computed for each face model. The model with the highest likelihood reveals the identity of the unknown face.2.12.2.The Volumetric Frequency Representation of Face ModelA face model that incorporates both the three dimensional (3D) face structure and its two-dimensional representation are explained (face images). This model which represents a volumetric (3D) frequency representation (VFR) of the face , is constructed using range image of a human head. Making use of an extension of the projection Slice Theorem, the Fourier transform of any face image corresponds to a slice in the face VFR. For both pose estimation and face recognition a face image is indexed in the 3D VFR based on the correlation matching in a four dimensional Fourier space, parameterized over the elevation, azimuth, rotation in the image planeand the scale of faces.3.ConclusionThis paper discusses the different approaches which have been employed in automatic face recognition. In the geometrical based methods, the geometrical features are selected and the significant facial features are detected. The correlation based approach needs face template rather than the significant facial features. Singular value vectors and the properties of the SV vector provide the theoretical basis for using singular values as image features. The Karhunen-Loeve expansion works by projecting the face images which represents the significant variations among the known faces. Eigen values and Eigen vectors are involved in extracting the features in KLT. Neural network based approaches are more efficient when it contains no more than a few hundred weights. The Hidden Markov model optimizes the parameters to best describe the observations in the sense of maximizing the probability of observations given in the model .Some methods use the features for classification and few methods uses the distance measure from the nodal points. The drawbacks of the methods are also discussed based on the performance of the algorithms used in the approaches. Hence this will give some idea about the existing methods for automatic face recognition.中文译文人脸识别技术综述摘要人脸是心灵的指标。
人脸识别外文翻译---对于脸部识别系统研究遗传算法
外文科技资料翻译英文原文Research on a face recognition system by the genetic algorithmComputer vision and recognition is playing anincreasingly important role in modern intelligent control.Object detection is the first and most important step inobjectrecognition.Traditionally,a special object can berecognized by the template matching method,but the recognition speed has always been a problem.In thisarticle,animproved general genetic algorithm-based face recognitionsystem is proposed.The genetic algorithm(GA)has beenconsidered to be a robust and global searching method.Here,the chromosomes generated by GA contain the information needed to recognize the object.The purpose of thisarticle is to propose a practical method of face detection andrecognition.Finally,the experimental results,and a comparison with the traditional template matching method,andsome otherconsiderations,are also given.1 IntroductionIf we search on the web or in a conference proceedingsabout intelligent control,lots of papers and applications arepresented.Among them,image processing and recognitionoccupy a very large percentage.The higher the degree ofintelligence,the more important the image detection andrecognition technology.For controlling an intelligent system(autonomous mobile vehicle,robot,etc.),the most important element is thecontrol strategy,but before automatically making it move,image recognition is needed.For an intelligent control system,it is necessary to acquire information about the external world automatically by sensors,in order to recognize itsposition and the surrounding situation.A camera is one ofthe most important sensors for computer vision.That is tosay,the system endeavors to find out what is in an image(the environment of the robot)taken by the camera:trafficsigns,obstacles,guidelines,etc.The reliability and time-response of object detection andrecognition have a major influence on the performance andusability of the whole object recognitionsystem.1Thetemplatematching method is a practicable and reasonablemethod for object detection.2This article gives an improvement in the general template matching method.In addition,in order to search for the object of interestin an image,lots of data need to be processed.The geneticalgorithm(GA)has been considered to be a robust andglobal searching method(although it is sometimes said thatGA can not be used for finding the globaloptimization3).Here,the chromosomes generated by GA contain information about the image data,and the genetic and evolutionoperations are used to obtain the best match to the template:searching for the best match is the goal of this article.This thought emerged from the features of the GA,andthe need to recognize the faces of people easily and quicklyby an intelligent system.The single concept and features ofimage processing and the GA will not be introduced here,because there is already extensive literature on this subject.In this article,Sect.2 gives the encoding method of theGA and the experimental setting that is used.In Sect.3,theexperiment and the analysis are addressed.Some conclusions are given in Sect.4.Theory and experimental settingFor an image recognition system,the most interesting partthat has special features has first to be detected in the original image.This is called object detection.After that,thispart will be compared to a template to see if it is similar ornot.This is called object recognition.For example,if wewant to find a special person in an image,we first have todetect people in the image,and then recognize which one isthe person ofinterest(sometimes these two steps will beexecuted simultaneously).The whole procedure is shown inFig.1.Fig.1. Object recognition systemStatistical object recognition involves locating and isolating the targets in an image,and then identifying them bystatistical decision theory.One of the oldest techniques ofpattern recognition is matching filtering,4which allowsthe computation of a measure of thesimilarity between theoriginal image f(x,y)and a template h(x,y).Define themean-squared distancedxdy y x h y x f d fh ⎰⎰-=22))},(,({(1)dxdy y x h y x f R fh ),(),(⎰⎰=,if the image and template are normalized bydxdy y x h dxdy y x f),(),(22⎰⎰⎰⎰=(2) And thendxdy y x h y x f d fh 22)},(),({⎰⎰-= =dxdy y x h y x h y x f y x f ⎰⎰+-)},(),(),(2),({22=fh R dxdy y x f 2),(22-⎰⎰(3)For the right-hand side of Eq.3,the first term is constant,and thus fh R can measure as the least-squared similaritybetween the original image and the template.5If fh R has alarge value(which means that 2fh d is small enough),then theimage is judged to match thetemplate.If fh R is less than apreselected threshold,the recognition process will eitherrejectthe match or create a new pattern,which means thatthe similarity between the object in the original image andthe template is not satisfied.2.1 Genetic encodingAs introduced above,the chromosomes generated by theGA contain information about the image data,so the firststep is to encode the image data into a binarystring.6Theparameters of the center of a face(x,y)in the originalimage,the rate of scale to satisfy eq.2,and the rotatingan gleθare encoded into the elements of a gene.Som e important parameters of the GA used here are given inTable 1,and the search field and region are given in Table2.As shown in Table2,one chromosome contains 4 bytes:the coordinate(x,y)in the original image,the rate of scale,and the rotation angleθ.2.2 Experimental settingThe experiment is done by first loading the original and thetemplate images.The GA is used to find whether or notthere is the object of a template in the original image.If theanswer isYES,then in the original image the result givesthe coordinates of the center of the object,the scale,and therotation angle from the template.For comparison,thegeneral template matching method is also presented.7The execution time shows the effectiveness of the GA-based recognition method.Figures 2 and 3 are the original images and the templatesfor the experiment.The values are the width×height inpixels of the image.In Fig.2,three images are presented,the content and size of which are different.Figure 2a hastwo faces(the faces of a person and a toy),Fig.2b shows aface tipped to one side,and the person in Fig.2c wears a hatand the background is more complicated than in Figs.2a and b.The two templates in Fig.3 are not extracted from thesame image.For normal use,the template should be extracted as the average of several feature images.In Fig.4,the template(a)-0 is generated from(a)-1,(a)-2,and(a)-3,and takes the average value of the gray levels from the threemodels.The same is also true for(b)-0.Fig.2.Three original images(max_x×max_y).a 238×170.b 185×196.c 275×225Fig.3.Templates for matching(temp_x ×temp_y).a Template 1.b Template 23 Experiment and comparisonThe genetic operations and GA parameters are presentedin Table 1 and Table 2.The fitness is defined as255)_()_(),(),,,(0.1_0_0⨯⨯--=∑∑==y temp x temp j i temp rate y x f fitness y temp j x temp i θ(4)In Eq.4,),(j i temp is the gray level of the coordinates ),(j i in the template image,the width and height of which are x temp _ and y temp _.),,,(θrate y x f gives the gray level in theoriginal image,the coordinates of which are calculated bytranslation from ),(y x ,and by changing the scale and the rotation angleθfrom the template.Since the images are256gray-level images,in Eq.4,division by 255 ensures that theresulting fitness is between 0 and 1.The maximum numberof generations is limited to 300,and the threshold of thematching rate is set to 0.9.6That is to say,if within 300generations the matching rate can reach 0.9,then it is saidthat the template is found in the original image(the template matched the original image by thethreshold).Otherwise,the result gives the best match until the trainingreaches the 300th generation.The results of GA-based face recognition are given inFig.6 and Table 3.Figure 6a,c and d are searched to matchthe template Fig.3a,while Fig.6b is matched to Fig.3b.Figure 6a and b reach the matching rate 0.9 within 300generations,while Fig.6c and d cannot reach the matchingrate 0.9 within 300 generations(the best match is given inTable 3).In the images in Fig.6a–c,we see that the resultgiven matches the template well.The coordinates x,therate of scale,and the angle of rotationθhave been foundcorrectly,but for(y),Fig.6d,the result is not very satisfactory.The reason for this is that the template Fig.3a cannotrepresent the face of interest at all times.That is to say,although the person to be recognized in different imagesis the same,the template cannot give the features for thisperson at all times(different appearance,etc.),and in allconditions.(The creation of the template is shown in Fig.4.)A second reason is that the algorithm itself has some problems.For example,by using a GA-based recognitionmethod,the settings of the search field(in this paper,)yx is selected),the determination of the geneticrate,(,,operations,and the selection and optimization of the fitness function all have a strong effect on the level of recognition of theresultant image.Fig.4.Creation of templateFor the purpose of comparing the effects of the GA-based algorithm,the result of the general matching method7is also presented.From Fig.5,we see that although both theoriginal image(the top-left image)and the template(thetop-right image)are simplified by binarization,the matching time is 1 min 22 s.The recognizable result is the bottomleft image in Fig.5.Fig.5.Result of searching by a GA4 ConclusionsIn this article,the GA-based image recognition method istested,and a comparison with the general matching methodis presented.As we know,the GA starts with an initial set of randomsolutions called thepopulation.Each individual in thepopulation is called a chromosome,and represents a solution to the problem.By stochastic search techniques basedon the mechanism of natural selection and natural genetics,genetic operations(crossover and mutation)and evolutionaryoperations(selecting or rejecting)are used to search forthe best solution.8In this article,the chromosomes generated by the GAcontain information about the image,and we use the genetic operators to obtain the best match between the originalimage and the template.The parameters are the coordinates(x,y)of the center of the object in the original image,the rate of scale,and the angle of ro tationθ.In fact,translation,scale,and rotation are the three maininvariant moments in the field of pattern recognition.9However,for face recognition,the facial features are difficult toextract,and are calculated by the general pattern recognition theory and method.10Even these three main invariant moments will not be invariant because the facial expressionis changed in different images.Thus,recognition only gives the best matching resultwithin an upper predetermined threshold.Both the GA-based method and the general template matching methodare presented here,and the comparison with the traditionalpattern matching method shows thatthe recognition is satisfactory,although under some conditions the result is notvery good(Fig.6d).Based on the results of the experiments described here,future workwillemphasize(i)optimizing the fields of chromosomes,and(ii)improving the fitness function by addingsome terms to it.This work is important and necessary inorder to improve the GA-based face recognition system.References1.Sugisaka M,Fan X(2002)Development of a face recognitionsystem for the life robot.Proceedings of the 7th InternationalSymposium on Artificial Life andRobotics,Oita,Japan,vol 2,Shubundo Insatsu Co.Ltd.,pp 538–5412.Castleman K(1998)Digital image processing.Original editionpublished by Prentice Hall;a Simon&Schuster Press of TsinghuaUniversity,China3.Iba H(1994)Foundation of genetic algorithm:solution of mysticGA(in Japanese).Omu Press4.Deguchi K,Takahashi I(1999)Image-based simultaneous controlof robot and target object motion by direct-image interpretation.Proceedings of the 1999 IEEE/RSJ International Conference onIntelligent Robot and Systems,Kyongju,Korea,vol 1,pp 375–3805.Jaehne B(1995)Digital image processing:concepts,algorithms,and scientific applications,3rd edn.Springer Berlin,Heidelberg,Germany6.Agui T,Nagao T(2000)Introduction to image processing usingprogramming language C(in Japanese).Shoko-do Press7.Ishibashi’s studying room of C++(inJapanese)./ishidate/vcpp.htm8.Gen M,Cheng R(1997)Genetic algorithms and engineeringdesign.Wiley-Interscience,New York9.Agui T,Nagao T(1992)Image processing and recognition(inJapanese).Syokoudou Press10.Takimoto H,Mitsukura T,Fukumi M,et al.(2002)A design of aface detection system based on the feature extraction method.Proceedings of the 12th Symposium onFuzzy,Artificial Intelligence,Neural Networks and ComputationalIntelligence,Saga,Japan,pp 409–410中文译文对于脸部识别系统研究遗传算法基于计算机视觉的手势识别对于当今智能控制起着非常重要的作用。
人脸识别的作文英文
人脸识别的作文英文Face recognition technology is becoming more and more common in our daily lives. It can be used for security purposes, such as unlocking smartphones or accessing secure areas. It can also be used for more personal reasons, like tagging friends in photos on social media.Some people have concerns about the privacyimplications of face recognition technology. They worrythat their faces could be scanned and stored without their consent, leading to potential misuse of their personal information.On the other hand, advocates of face recognition technology argue that it can help make our lives more convenient and secure. For example, it can help law enforcement agencies identify suspects more quickly and accurately.One of the biggest challenges with face recognitiontechnology is ensuring its accuracy. There have been cases where the technology has misidentified individuals, leading to false accusations and misunderstandings.Despite its potential drawbacks, face recognition technology is likely to become even more prevalent in the future. As the technology continues to improve, it will be interesting to see how it is used and regulated indifferent parts of the world.。
人脸识别英语作文
Face Recognition: A Double-Edged Sword ofModern TechnologyIn the age of digital transformation, face recognition technology has become a ubiquitous part of our daily lives. From unlocking smartphones to accessing secure areas, this cutting-edge technology has revolutionized the way we interact with the world. However, as with any technology, face recognition presents both remarkable benefits and significant concerns.The primary benefit of face recognition is its convenience and efficiency. Gone are the days of fumbling with keys or forgetting passwords. With a simple glance at a camera, individuals can gain access to their devices or buildings with ease. This便利has streamlined numerous processes, from airport security checks to retail payments, significantly improving the user experience.Moreover, face recognition has immense potential in law enforcement and national security. It can assist in identifying criminals, tracking fugitives, and even preventing crimes by recognizing suspicious activities. Thetechnology has been used successfully in various countries to solve crimes and keep the public safe.However, the rise of face recognition technology also raises serious privacy concerns. In a world where every face can be captured and identified, the potential for misuse and abuse is alarming. Governments and corporations could potentially misuse this data for surveillance, stalking, or even discrimination. Furthermore, the storage and protection of this sensitive information aresignificant challenges that need to be addressed.Moreover, the accuracy of face recognition technologyis not without flaws. While it can be highly accurate in ideal conditions, factors such as lighting, angles, and even facial hair can affect recognition rates. This can lead to false positives or negatives, potentially resulting in embarrassing misidentifications or even serious consequences such as wrongful arrests.Additionally, the ethical implications of face recognition are profound. The technology has the potential to create a divide between those who are recognized and those who are not. This could lead to discriminationagainst certain groups, such as minorities or individuals with disabilities, who may be more difficult to identify. In conclusion, face recognition technology is a powerful tool that has brought remarkable benefits to our lives. However, it also poses significant challenges and concerns that need to be addressed. As we continue to embrace this technology, it is crucial that we do so with a balanced perspective, ensuring that its benefits are maximized while minimizing its negative impacts on privacy, security, and equality.**人脸识别：现代科技的双刃剑**在数字化转型的时代，人脸识别技术已经成为我们日常生活中无处不在的一部分。
人脸识别外文翻译参考文献
人脸识别外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)译文：基于PAC的实时人脸检测和跟踪方法摘要：这篇文章提出了复杂背景条件下，实现实时人脸检测和跟踪的一种方法。
这种方法是以主要成分分析技术为基础的。
为了实现人脸的检测，首先，我们要用一个肤色模型和一些动作信息(如：姿势、手势、眼色)。
然后，使用PAC技术检测这些被检验的区域，从而判定人脸真正的位置。
而人脸跟踪基于欧几里德（Euclidian）距离的，其中欧几里德距离在位于以前被跟踪的人脸和最近被检测的人脸之间的特征空间中。
用于人脸跟踪的摄像控制器以这样的方法工作：利用平衡/（pan/tilt）平台，把被检测的人脸区域控制在屏幕的中央。
这个方法还可以扩展到其他的系统中去，例如电信会议、入侵者检查系统等等。
1.引言视频信号处理有许多应用，例如鉴于通讯可视化的电信会议，为残疾人服务的唇读系统。
在上面提到的许多系统中，人脸的检测喝跟踪视必不可缺的组成部分。
在本文中，涉及到一些实时的人脸区域跟踪[1-3]。
一般来说，根据跟踪角度的不同，可以把跟踪方法分为两类。
有一部分人把人脸跟踪分为基于识别的跟踪喝基于动作的跟踪，而其他一部分人则把人脸跟踪分为基于边缘的跟踪和基于区域的跟踪[4]。
基于识别的跟踪是真正地以对象识别技术为基础的，而跟踪系统的性能是受到识别方法的效率的限制。
基于动作的跟踪是依赖于动作检测技术，且该技术可以被分成视频流（optical flow）的（检测）方法和动作—能量（motion－energy）的（检测）方法。
基于边缘的（跟踪）方法用于跟踪一幅图像序列的边缘，而这些边缘通常是主要对象的边界线。
然而，因为被跟踪的对象必须在色彩和光照条件下显示出明显的边缘变化，所以这些方法会遭遇到彩色和光照的变化。
此外，当一幅图像的背景有很明显的边缘时，（跟踪方法）很难提供可靠的（跟踪）结果。
当前很多的文献都涉及到的这类方法时源于Kass et al.在蛇形汇率波动[5]的成就。
人脸识别外文文献
Method of Face Recognition Based on Red-BlackWavelet Transform and PCAYuqing He, Huan He， and Hongying YangDepartment of Opto-Electronic Engineering，Beijing Institute of Technology, Beijing， P.R. China, 10008120701170@。
cnAbstract。
With the development of the man—machine interface and the recogni—tion technology， face recognition has became one of the most important research aspects in the biological features recognition domain. Nowadays, PCA（Principal Components Analysis） has applied in recognition based on many face database and achieved good results. However， PCA has its limitations: the large volume of computing and the low distinction ability。
In view of these limitations, this paper puts forward a face recognition method based on red—black wavelet transform and PCA. The improved histogram equalization is used to realize image pre-processing in order to compensate the illumination. Then, appling the red—black wavelet sub—band which contains the information of the original image to extract the feature and do matching。
人脸识别英文文献
A Parallel Framework for Multilayer Perceptron for Human FaceRecognitionDebotosh Bhattacharjee debotosh@ Reader,Department of Computer Science and Engineering,Jadavpur University,Kolkata- 700032, India.Mrinal Kanti Bhowmik mkb_cse@yahoo.co.in Lecturer,Department of Computer Science and Engineering,Tripura University (A Central University),Suryamaninagar- 799130, Tripura, India.Mita Nasipuri mitanasipuri@ Professor,Department of Computer Science and Engineering,Jadavpur University,Kolkata- 700032, India.Dipak Kumar Basu dipakkbasu@ Professor, AICTE Emeritus Fellow,Department of Computer Science and Engineering,Jadavpur University,Kolkata- 700032, India.Mahantapas Kundu mkundu@cse.jdvu.ac.in Professor,Department of Computer Science and Engineering,Jadavpur University,Kolkata- 700032, India.AbstractArtificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Both the structures wereimplemented and tested for face recognition purpose and experimental results show that the OCON structure performs better than the generally used ACON ones in term of training convergence speed of the network. Unlike the conventional sequential approach of training the neural networks, the OCON technique may be implemented by training all the classes of the face images simultaneously.Keywords:Artificial Neural Network, Network architecture, All-Class-in-One-Network (ACON), One-Class-in-One-Network (OCON), PCA, Multilayer Perceptron, Face recognition.1. INTRODUCTIONNeural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze [1]. This proposed work describes the way by which an Artificial Neural Network (ANN) can be designed and implemented over a parallel or distributed environment to reduce its training time. Generally, an ANN goes through three different steps: training of the network, testing of it and final use of it. The final structure of an ANN is generally found out experimentally. This requires huge amount of computation. Moreover, the training time of an ANN is very large, when the classes are linearly non-separable and overlapping in nature. Therefore, to save computation time and in order to achieve good response time the obvious choice is either a high-end machine or a system which is collection of machines with low computational power.In this work, we consider multilayer perceptron (MLP) for human face recognition, which has many real time applications starting from automatic daily attendance checking, allowing the authorized people to enter into highly secured area, in detecting and preventing criminals and so on. For all these cases, response time is very critical. Face recognition has the benefit of being passive, nonintrusive system for verifying personal identity. The techniques used in the best face recognition systems may depend on the application of the system.Human face recognition is a very complex pattern recognition problem, altogether. There is no stability in the input pattern due to different expressions, adornments in the input images. Sometimes, distinguishing features appear similar and produce a very complex situation to take decision. Also, there are several other that make the face recognition task complicated. Some of them are given below.a) Background of the face image can be a complex pattern or almost same as the color of theface.b) Different illumination level, at different parts of the image.c) Direction of illumination may vary.d) Tilting of face.e) Rotation of face with different angle.f) Presence/absence of beard and/or moustacheg) Presence/Absence of spectacle/glasses.h) Change in expressions such as disgust, sadness, happiness, fear, anger, surprise etc.i) Deliberate change in color of the skin and/or hair to disguise the designed system.From above discussion it can now be claimed that the face recognition problem along with face detection, is very complex in nature. To solve it, we require some complex neural network, which takes large amount of time to finalize its structure and also to settle its parameters.In this work, a different architecture has been used to train a multilayer perceptron in faster way. Instead of placing all the classes in a single network, individual networks are used for each of theclasses. Due to lesser number of samples and conflicts in the belongingness of patterns to their respective classes, a later model appears to be faster in comparison to former.2. ARTIFICIAL NEURAL NETWORKArtificial neural networks (ANN) have been developed as generalizations of mathematical models of biological nervous systems. A first wave of interest in neural networks (also known as connectionist models or parallel distributed processing) emerged after the introduction of simplified neurons by McCulloch and Pitts (1943).The basic processing elements of neural networks are called artificial neurons, or simply neurons or nodes. In a simplified mathematical model of the neuron, the effects of the synapses are represented by connection weights that modulate the effect of the associated input signals, and the nonlinear characteristic exhibited by neurons is represented by a transfer function. The neuron impulse is then computed as the weighted sum of the input signals, transformed by the transfer function. The learning capability of an artificial neuron is achieved by adjusting the weights in accordance to the chosen learning algorithm. A neural network has to be configured such that the application of a set of inputs produces the desired set of outputs. Various methods to set the strengths of the connections exist. One way is to set the weights explicitly, using a priori knowledge. Another way is to train the neural network by feeding it teaching patterns and letting it change its weights according to some learning rule. The learning situations in neural networks may be classified into three distinct sorts. These are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning,an input vector is presented at the inputs together with a set of desired responses, one for each node, at the output layer. A forward pass is done, and the errors or discrepancies between the desired and actual response for each node in the output layer are found. These are then used to determine weight changes in the net according to the prevailing learning rule. The term supervised originates from the fact that the desired signals on individual output nodes are provided by an external teacher [3]. Feed-forward networks had already been used successfully for human face recognition. Feed-forward means that there is no feedback to the input. Similar to the way that human beings learn from mistakes, neural networks also could learn from their mistakes by giving feedback to the input patterns. This kind of feedback would be used to reconstruct the input patterns and make them free from error; thus increasing the performance of the neural networks. Of course, it is very complex to construct such types of neural networks. These kinds of networks are called as auto associative neural networks. As the name implies, they use back-propagation algorithms. One of the main problems associated with back-propagation algorithms is local minima. In addition, neural networks have issues associated with learning speed, architecture selection, feature representation, modularity and scaling. Though there are problems and difficulties, the potential advantages of neural networks are vast. Pattern recognition can be done both in normal computers and neural networks. Computers use conventional arithmetic algorithms to detect whether the given pattern matches an existing one. It is a straightforward method. It will say either yes or no. It does not tolerate noisy patterns. On the other hand, neural networks can tolerate noise and, if trained properly, will respond correctly for unknown patterns. Neural networks may not perform miracles, but if constructed with the proper architecture and trained correctly with good data, they will give amazing results, not only in pattern recognition but also in other scientific and commercial applications [4].2A. Network ArchitectureThe computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Once a network is trained properly there is no need to devise an algorithm in order to perform a specific task; i.e. no need to understand the internal mechanisms of that task. The architecture of any neural networks generally used is All-Class-in-One-Network (ACON), where all the classes are lumped into one super-network. Hence, the implementation of such ACON structure in parallel environment is not possible. Also, the ACON structure has some disadvantages like the super-network has the burden to simultaneously satisfy all the error constraints by which the number of nodes in the hidden layers tends to be large. The structure of the network is All-Classes-in-One-Network (ACON), shown in Figure 1(a) where one single network is designed to classify all the classes but in One-Class-in-One-Network(OCON), shown in Figure 1(b) a single network is dedicated to recognize one particular class. For each class, a network is created with all the training samples of that class as positive examples, called the class-one, and the negative examples for that class i.e. exemplars from other classes, constitute the class-two. Thus, this classification problem is a two-class partitioning problem. So far, as implementation is concerned, the structure of the network remains the same for all classes and only the weights vary. As the network remains same, weights are kept in separate files and the identification of input image is made on the basis of feature vector and stored weights applied to the network one by one, for all the classes.(a)(b)Figure 1: a) All-Classes-in-One-Network (ACON) b) One-Class-in-One-Network (OCON). Empirical results confirm that the convergence rate of ACON degrades drastically with respect to the network size because the training of hidden units is influenced by (potentially conflicting) signals from different teachers. If the topology is changed to One Class in One Network (OCON) structure, where one sub-network is designated and responsible for one class only then each sub-network specializes in distinguishing its own class from the others. So, the number of hidden units is usually small.2B. Training of an ANNIn the training phase the main goal is to utilize the resources as much as possible and speed-up the computation process. Hence, the computation involved in training is distributed over the system to reduce response time. The training procedure can be given as:(1) Retrieve the topology of the neural network given by the user,(2) Initialize required parameters and weight vector necessary to train the network,(3) Train the network as per network topology and available parameters for all exemplars of different classes,(4) Run the network with test vectors to test the classification ability,(5) If the result found from step 4 is not satisfactory, loop back to step 2 to change the parameters like learning parameter, momentum, number of iteration or even the weight vector,(6) If the testing results do not improve by step 5, then go back to step 1,(7) The best possible (optimal) topology and associated parameters found in step 5 and step 6 are stored.Although we have parallel systems already in use but some problems cannot exploit advantages of these systems because of their inherent sequential execution characteristics. Therefore, it is necessary to find an equivalent algorithm, which is executable in parallel.In case of OCON, different individual small networks with least amount of load, which are responsible for different classes (e.g. k classes), can easily be trained in k different processors and the training time must reduce drastically. To fit into this parallel framework previous training procedure can be modified as follows:(1) Retrieve the topology of the neural network given by the user,(2) Initialize required parameters and weight vector necessary to train the network,(3) Distribute all the classes (say k) to available processors (possibly k) by some optimal process allocation algorithm,(4) Ensure the retrieval the exemplar vectors of respective classes by the corresponding processors,(5) Train the networks as per network topology and available parameters for all exemplars of different classes,(6) Run the networks with test vectors to test the classification ability,(7) If the result found from step 6 is not satisfactory, loop back to step 2 to change the parameters like learning parameter, momentum, number of iteration or even the weight vector,(8) If the testing results do not improve by step 5, then go back to step 1,(9) The best possible (optimal) topology and associated parameters found in step 7 and step 8 are stored,(10) Store weights per class with identification in more than one computer [2].During the training of two different topologies (OCON and ACON), we used total 200 images of 10 different classes and the images are with different poses and also with different illuminations. Sample images used during training are shown Figure 2. We implemented both the topologies using MATLAB. At the time of training of our systems for both the topologies, we set maximum number of possible epochs (or iterations) to 700000. The training stops if the number of iterations exceeds this limit or performance goal is met. Here, performance goal was considered as 10-6. We have taken total 10 different training runs for 10 different classes for OCON and one single training run for ACON for 10 different classes. In case of the OCON networks, performance goal was met for all the 10 different training cases, and also in lesser amount of time than ACON. After the completion of training phase of our two different topologies we tested our both the network using the images of testing class which are not used in training.2C. Testing PhaseDuring testing, the class found in the database with minimum distance does not necessarily stop the testing procedure. Testing is complete after all the registered classes are tested. During testing some points were taken into account, those are:(1) The weights of different classes already available are again distributed in the available computer to test a particular image given as input,(2) The allocation of the tasks to different processors is done based on the testing time and inter-processor communication overhead. The communication overhead should be much less than the testing time for the success of the distribution of testing, and(3) The weight vector of a class matter, not the computer, which has computed it.The testing of a class can be done in any computer as the topology and the weight vector of that class is known. Thus, the system can be fault tolerant [2]. At the time of testing, we used total 200 images. Among 200 images 100 images are taken from the same classes those are used during the training and 100 images from other classes are not used during the training time. In the both topology (ACON and OCON), we have chosen 20 images for testing, in which 10 images from same class those are used during the training as positive exemplars and other 10 images are chosen from other classes of the database as negative exemplars.2D. Performance measurementPerformance of this system can be measured using following parameters:(1) resource sharing: Different terminals remain idle most of the time can be used as a part of this system. Once the weights are finalized anyone in the net, even though not satisfying the optimal testing time criterion, can use it. This can be done through Internet attempting to end the “tyranny of geography”,(2) high reliability: Here we will be concerned with the reliability of the proposed system, not the inherent fault tolerant property of the neural network. Reliability comes from the distribution of computed weights over the system. If any of the computer(or processor) connected to the network goes down then the system works Some applications like security monitoring system, crime prevention system require that the system should work, whatever may be the performance, (3) cost effectiveness: If we use several small personal computers instead of high-end computing machines, we achieve better price/performance ratio,(4) incremental growth: If the number of classes increases, then the complete computation including the additional complexity can be completed without disturbing the existing system. Based on the analysis of performance of our two different topologies, if we see the recognition rates of OCON and ACON in Table 1 and Table 2 OCON is showing better recognition rate than ACON. Comparison in terms of training time can easily be observed in figures 3 (Figure 3 (a) to (k)). In case of OCON, performance goals met for 10 different classes are 9.99999e-007, 1e-006, 9.99999e-007, 9.99998e-007, 1e-006, 9.99998e-007,1e-006, 9.99997e-007, 9.99999e-007 respectively, whereas for ACON it is 0.0100274. Therefore, it is pretty clear that OCON requires less computational time to finalize a network to use.3. PRINCIPAL COMPONENT ANALYSISThe Principal Component Analysis (PCA) [5] [6] [7] uses the entire image to generate a set of features in the both network topology OCON and ACON and does not require the location of individual feature points within the image. We have implemented the PCA transform as a reduced feature extractor in our face recognition system. Here, each of the visual face images is projected into the eigenspace created by the eigenvectors of the covariance matrix of all the training images for both the ACON and OCON networks. Here, we have taken the number of eigenvectors in the eigenspace as 40 because eigenvalues for other eigenvectors are negligible in comparison to the largest eigenvalues.4. EXPERIMENTS RESULTS USING OCON AND ACONThis work has been simulated using MATLAB 7 in a machine of the configuration 2.13GHz Intel Xeon Quad Core Processor and 16 GB of Physical Memory. We have analyzed the performance of our method using YALE B database which is a collection of visual face images with various poses and illumination.4A. YALE Face Database BThis work has been simulated using MATLAB 7 in a machine of the configuration 2.13GHz Intel Xeon Quad Core Processor and 16 GB of Physical Memory. We have analyzed the performance of our method using YALE B database which is a collection of visual face images with various poses and illumination.This database contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions). For every subject in a particular pose, an image with ambient (background) illumination was also captured. Hence, the total number of images is 5850. The total size of the compressed database is about 1GB. The 65 (64 illuminations + 1 ambient) images of a subject in a particular pose have been "tarred" and "gzipped" into a single file. There were 47 (out of 5760) images whose corresponding strobe did not go off. These images basically look like the ambient image of the subject in a particular pose. The images in the database were captured using a purpose-built illumination rig. This rig is fitted with 64 computer controlled strobes. The 64 images of a subject in a particular pose were acquired at camera frame rate (30 frames/second) in about 2 seconds, so there is only small change in head pose and facial expression for those 64 (+1 ambient) images. The image with ambient illumination was captured without a strobe going off. For each subject, images were captured under nine different poses whose relative positions are shown below. Note the pose 0 is the frontal pose. Poses 1, 2, 3, 4, and 5 were about 12 degrees from the camera optical axis (i.e., from Pose 0), while poses 6, 7, and 8 were about 24 degrees. In the Figure 2 sample images of per subject per pose with frontal illumination. Note that the position of a face in an image varies from pose to pose but is fairly constant within the images of a face seen in one of the 9 poses, since the 64 (+1 ambient) images were captured in about 2 seconds. The acquired images are 8-bit (gray scale) captured with a Sony XC-75 camera (with a linear response function) and stored in PGM raw format. The size of each image is 640(w) x 480 (h) [9].In our experiment, we have chosen total 400 images for our experiment purpose. Among them 200 images are taken for training and other 200 images are taken for testing purpose from 10 different classes. In the experiment we use total two different networks: OCON and ACON. All the recognition results of OCON networks are shown in Table 1, and all the recognition results of ACON network are shown in Table 2. During training, total 10 training runs have been executed for 10 different classes. We have completed total 10 different testing for OCON network using 20 images for each experiment. Out of those 20 images, 10 images are taken form the same classes those were used during training, which acts as positive exemplars and rest 10 images are taken from other classes that acts as negative exemplars for that class. In case of OCON, system achieved 100% recognition rate for all the classes. In case of the ACON network, only one network is used for 10 different classes. During the training we achieved 100% as the highest recognition rate, but like OCON network not for all the classes. For ACON network, on an average, 88% recognition rate was achieved.Figure 2: Sample images of YALE B database with different Pose and different illumination.Class Total number oftesting images Number of imagesfrom the trainingclassNumber ofimages fromother classesRecognitionrateClass-1 20 10 10 100% Class-2 20 10 10 100% Class-3 20 10 10 100% Class-4 20 10 10 100% Class-5 20 10 10 100% Class-6 20 10 10 100% Class-7 20 10 10 100% Class-8 20 10 10 100% Class-9 20 10 10 100% Class-10 20 10 10 100%Table 1: Experiments Results for OCON.Class Total number oftesting images Number of imagesfrom the trainingclassNumber ofimages fromother classesRecognitionrateClass - 1 20 10 10 100%Class - 2 20 10 10 100%Class - 3 20 10 10 90%Class - 4 20 10 10 80%Class - 5 20 10 10 80%Class - 6 20 10 10 80%Class - 7 20 10 10 90%Class - 8 20 10 10 100%Class - 9 20 10 10 90%Class-10 20 10 10 70%Table 2: Experiments results for ACON.In the Figure 3, we have shown all the performance measure and reached goal during 10 different training runs in case of OCON network and also one training phase of ACON network.We set highest epochs 700000, but during the training, in case of all the OCON networks, performance goal was met before reaching maximum number of epochs. All the learning rates with required epochs of OCON and ACON networks are shown at column two of Table 3.In case of the OCON network, if we combine all the recognition rates we have the average recognition rate is 100%. But in case of ACON network, 88% is the average recognition rate i.e.we can say that OCON showing better performance, accuracy and speed than ACON. Figure 4 presents a comparative study on ACON and OCON results.Total no. of iterations Learning Rate(lr)Class Figures Network Used 290556 lr > 10-4 Class – 1 Figure 3(a) 248182 lr =10-4 Class – 2 Figure 3(b) 260384 lr =10-5 Class – 3 Figure 3(c) 293279 lr < 10-4 Class - 4 Figure 3(d) 275065 lr =10-4 Class - 5 Figure 3(e) 251642 lr =10-3 Class – 6 Figure 3(f) 273819 lr =10-4 Class – 7 Figure 3(g) 263251 lr < 10-3Class – 8 Figure 3(h) 295986 lr < 10-3 Class – 9 Figure 3(i) 257019 lr > 10-6 Class - 10 Figure 3(j) OCON Highest epochreached(7, 00, 000)Performance goal not met For all Classes (class -1,…,10) Figure 3(k) ACONTable 3: Learning Rate vs. Required Epochs for OCON and ACON.Figure 3 (a) Class – 1 of OCON Network.Figure 3 (b) Class – 2 of OCON Network.Figure 3 (c) Class – 3 of OCON Network.D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. KunduFigure 3 (d) Class – 4 of OCON Network.Figure 3 (e) Class – 5 of Ocon Network.International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6)11D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. KunduFigure 3 (f) Class – 6 of OCON Network.Figure 3 (g) Class – 7 of OCON Network.International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6)12D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. KunduFigure 3 (h) Class – 8 of OCON Network.Figure 3 (i) Class – 9 of OCON Network.International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6)13D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. KunduFigure 3 (j) Class – 10 of OCON Network.3 (k) of ACON Network for all the classes.International Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6)14D. Bhattacharjee, M. K. Bhowmik, M. Nasipuri, D. K. Basu & M. KunduFigure 4: Graphical Representation of all Recognition Rate using OCON and ACON Network. The OCON is an obvious choice in terms of speed-up and resource utilization. The OCON structure of neural network makes it most suitable for incremental training, i.e., network upgrading upon adding/removing memberships. One may argue that compared to ACON structure, the OCON structure is slow in retrieving time when the number of classes is very large. This is not true because, as the number of classes increases, the number of hidden neurons in the ACON structure also tends to be very large. Therefore ACON is slow. Since the computation time of both OCON and ACON increases as number of classes grows, a linear increasing of computation time is expected in case of OCON, which might be exponential in case of ACON.5. CONCLUSIONIn this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Both the structures were implemented and tested for face recognition purpose and experimental results show that the OCON structure performs better than the generally used ACON ones in term of training convergence speed of the network. Moreover, the inherent non-parallel nature of ACON has compelled us to use OCON for the complex pattern recognition task like human face recognition.ACKNOWLEDGEMENTSecond author is thankful to the project entitled “Development of Techniques for Human Face Based Online Authentication System Phase-I” sponsored by Department of Information Technology under the Ministry of Communications and Information Technology, New Delhi110003, Government of India Vide No. 12(14)/08-ESD, Dated 27/01/2009 at the Department of Computer Science & Engineering, Tripura University-799130, Tripura (West), India for providingInternational Journal of Computer Science and Security (IJCSS), Volume (3): Issue (6)15。
面部识别在中国的应用英语作文
面部识别在中国的应用英语作文Here is an English essay on the topic of facial recognition technology and its applications in China, with a word count of over 1000 words.Facial recognition technology has been rapidly advancing in recent years and has found numerous applications across various industries. China, in particular, has been at the forefront of adopting and implementing this technology on a large scale. The widespread use of facial recognition in China raises important questions about privacy, security, and the ethical implications of such technologies.One of the primary areas where facial recognition has been extensively utilized in China is in the realm of public security. The Chinese government has invested heavily in building a vast surveillance network, with millions of CCTV cameras equipped with facial recognition capabilities. This system, often referred to as the "Skynet" project, aims to enhance public safety and security by identifying and tracking individuals in real-time. The technology has been employed to monitor crowds, detect criminal activities, and even identify suspected terrorists.The extensive use of facial recognition in public spaces has drawnsignificant criticism from human rights organizations and privacy advocates. They argue that the lack of robust privacy safeguards and the potential for abuse of such technology pose a serious threat to individual liberties and civil liberties. The Chinese government, however, maintains that the benefits of enhanced public safety outweigh the privacy concerns and that the system is necessary to maintain social stability and prevent criminal activities.Another area where facial recognition has been widely adopted in China is in the commercial sector. Numerous businesses, from retail stores to financial institutions, have integrated facial recognition technology into their operations. In shopping malls, for example, facial recognition is used to track customer behavior, analyze shopping patterns, and personalize marketing efforts. Banks and financial institutions have also implemented facial recognition for identity verification and security purposes, reducing the reliance on traditional methods like passwords and PIN codes.The integration of facial recognition in the commercial sector has raised concerns about data privacy and the potential misuse of personal information. There are fears that companies may use the collected data for targeted advertising, profiling, or even sharing it with third parties without the individual's consent. The Chinese government has attempted to address these concerns by introducing regulations and guidelines for the use of facial recognitiontechnology, but the enforcement and oversight of these measures remain a challenge.The use of facial recognition in China has also extended to the realm of social control and surveillance. The government has implemented a social credit system that utilizes various data sources, including facial recognition, to monitor and evaluate the behavior of citizens. This system rewards or penalizes individuals based on their compliance with social and political norms, raising concerns about the erosion of personal freedoms and the potential for abuse of power.Moreover, facial recognition technology has been employed in the education sector in China. Schools have installed cameras equipped with facial recognition to track student attendance, monitor their behavior, and even assess their attentiveness during class. While the stated purpose of these measures is to improve educational outcomes and enhance student safety, critics argue that they infringe on the privacy and autonomy of young individuals and may have long-term psychological impacts.Despite the widespread adoption of facial recognition technology in China, the public's perception of its use is somewhat mixed. While some citizens appreciate the enhanced security and convenience it provides, others express concerns about the potential for abuse andthe erosion of privacy rights. The Chinese government has attempted to address these concerns by emphasizing the importance of the technology in maintaining social stability and public safety, but the ongoing debates and discussions surrounding the ethical implications of facial recognition continue.In conclusion, the application of facial recognition technology in China is a complex and multifaceted issue. While the technology has undoubtedly brought about improvements in public safety, security, and commercial efficiency, it has also raised significant concerns about privacy, civil liberties, and the potential for abuse of power. As the use of facial recognition continues to expand in China, it is crucial that policymakers, technology companies, and the public engage in a thoughtful and transparent dialogue to ensure that the benefits of this technology are balanced with the protection of individual rights and the preservation of democratic values.。
人脸识别在中国的应用英语作文
人脸识别在中国的应用英语作文The rapid advancements in technology have transformed various aspects of our daily lives and one of the most prominent applications is in the field of facial recognition. China, known for its technological prowess, has been at the forefront of implementing facial recognition systems across a wide range of sectors. From public security to commercial applications, the integration of this innovative technology has significantly impacted the lives of the Chinese population.One of the primary areas where facial recognition has found extensive use in China is in the realm of public security. The Chinese government has invested heavily in the development and deployment of extensive surveillance networks utilizing facial recognition technology. These systems are strategically placed in public spaces such as transportation hubs, shopping malls, and residential areas to aid in the identification and tracking of individuals. The goal is to enhance public safety and combat criminal activities by providing law enforcement agencies with a powerful tool to quickly identify and apprehend suspects.The implementation of facial recognition in China's public security sector has been met with both praise and criticism. Proponents argue that the technology has proven effective in deterring and solving crimes, leading to a safer environment for citizens. They cite examples of successful cases where facial recognition has helped locate missing persons, identify perpetrators of various offenses, and even prevent terrorist attacks. However, critics raise concerns about the potential infringement of individual privacy and the creation of a surveillance state, where the government can closely monitor the movements and activities of its citizens without their consent.Beyond the realm of public security, facial recognition technology has also found widespread application in the commercial sector in China. Retailers have embraced this technology to enhance the customer experience and improve operational efficiency. Facial recognition systems are used to identify and track customer behavior within stores, allowing for personalized recommendations, targeted marketing, and streamlined checkout processes. Additionally, the technology has been integrated into various payment systems, enabling customers to make purchases simply by using their faces as a form of identification, eliminating the need for cash or cards.The integration of facial recognition in the commercial sector has been largely welcomed by Chinese consumers, who have embracedthe convenience and efficiency it offers. However, some individuals have raised concerns about the potential misuse of their personal data and the lack of transparency regarding the collection and storage of such information.Another notable application of facial recognition in China is in the realm of social services. The government has implemented facial recognition systems to streamline various administrative processes, such as accessing healthcare services, applying for government benefits, and verifying identities for various transactions. This integration has aimed to enhance the efficiency and accessibility of these services, particularly for the elderly and individuals living in remote areas who may have limited access to traditional identification documents.While the implementation of facial recognition in social services has been praised for its ability to improve the lives of Chinese citizens, there are concerns about the potential for exclusion and the risk of data breaches that could compromise sensitive personal information.The adoption of facial recognition technology in China has also extended to the education sector. Schools and universities have implemented systems to track student attendance, monitor classroom behavior, and even identify individuals who may pose a threat to campus safety. This integration has been touted as a meansof enhancing academic performance and ensuring the well-being of students, but it has also raised concerns about the potential infringement of student privacy and the creation of a culture of constant surveillance.Furthermore, the use of facial recognition technology has found its way into the realm of transportation in China. Airports, train stations, and other transportation hubs have incorporated these systems to streamline security checks, identify individuals on watchlists, and improve overall efficiency in passenger movement. While this integration has been praised for its ability to enhance travel experiences, it has also raised concerns about the potential for data breaches and the centralization of personal information.The widespread adoption of facial recognition technology in China has not been without its challenges. Concerns have been raised about the potential for bias and discrimination within these systems, as well as the lack of robust data privacy regulations to protect the personal information of citizens. There have been instances where the technology has been shown to be less accurate in identifying individuals with certain ethnic or gender backgrounds, raising questions about the fairness and inclusivity of these systems.Despite these challenges, the Chinese government has remained committed to the continued development and deployment of facialrecognition technology across various sectors. The potential benefits of this technology, such as enhanced public safety, improved service delivery, and increased operational efficiency, have been the driving force behind its widespread adoption.However, as the use of facial recognition continues to expand in China, it is crucial that policymakers, technology companies, and the public engage in constructive dialogues to address the ethical and privacy concerns surrounding this technology. Striking a balance between the benefits of facial recognition and the protection of individual rights will be essential in ensuring that the implementation of this technology aligns with the values of a just and equitable society.In conclusion, the application of facial recognition technology in China has been extensive and multifaceted, spanning sectors such as public security, commerce, social services, education, and transportation. While the technology has brought about significant improvements in various aspects of life, it has also raised concerns about privacy, bias, and the potential for misuse. As China continues to embrace this transformative technology, it is imperative that the country addresses these challenges and ensures that the implementation of facial recognition aligns with the principles of ethical and responsible technological development.。
Paul Viola经典人脸检测算法论文翻译
级联阶段的构成首先是利用AdaBoost训练分类器，然后调整阈值使得负误视最大限度地减少。注意，默认AdaBoost的阈值旨在数据过程中产生低错误率。一般而言，一个较低的阈值会产生更高的检测速率和更高的正误视率。
对于人脸检测的任务，由AdaBoost选择的最初的矩形特征是有意义的且容易理解。选定的第一个特征的重点是眼睛区域往往比鼻子和脸颊区域更黑暗（见图3）。此特征的检测子窗口相对较大，并且某种程度上不受面部大小和位置的影响。第二个特征选择依赖于眼睛的所在位置比鼻梁更暗。
这两个特点显示在最上面一行，然后一个典型的调试面部叠加在底部一行。第一个特点，测量眼睛部区域和上脸颊地区的强烈程度的区别。该特征利用了眼睛部区域往往比脸颊更暗。第二个特点比较了眼睛区域与鼻梁的强度。
1.引言
本文汇集了新的算法和见解，构筑一个鲁棒性良好的极速目标检测框架。这一框架主要是体现人脸检测的任务。为了实现这一目标，我们已经建立了一个正面的人脸检测系统，实现了相当于已公布的最佳结果的检测率和正误视率，[16，12，15，11，1]。这种人脸检测系统区分人脸比以往的方法都要清楚，而且速度很快。通过对384×288像素的图像，硬件环境是常规700 MHz英特尔奔腾III，人脸检测速度达到了每秒15帧。在其它人脸检测系统中，一些辅助信息如视频序列中的图像差异，或在彩色图像中像素的颜色，被用来实现高帧率。而我们的系统仅仅使用一个单一的灰度图像信息实现了高帧速率。上述可供选择的信息来源也可以与我们的系统集成，以获得更高的帧速率。
人脸识别技术外文翻译文献编辑
文献信息文献标题：Face Recognition Techniques: A Survey（人脸识别技术综述）文献作者：V.Vijayakumari文献出处：《World Journal of Computer Application and Technology》, 2013,1(2):41-50字数统计：英文3186单词，17705字符；中文5317汉字外文文献Face Recognition Techniques: A Survey Abstract Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.Keywords: Face Recognition, Illumination, Authentication, Security1.IntroductionDeveloped in the 1960s, the first semi-automated system for face recognition required the administrator to locate features ( such as eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. In the 1970s, Goldstein, Armon, and Lesk used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solutions was that the measurements and locations were manually computed. The face recognition problem can be divided into two main stages: face verification (or authentication), and face identification (or recognition).The detection stage is the first stage; it includesidentifying and locating a face in an image. The recognition stage is the second stage; it includes feature extraction, where important information for the discrimination is saved and the matching where the recognition result is given aid of a face database.2.Methods2.1.Geometric Feature Based MethodsThe geometric feature based approaches are the earliest approaches to face recognition and detection. In these systems, the significant facial features are detected and the distances among them as well as other geometric characteristic are combined in a feature vector that is used to represent the face. To recognize a face, first the feature vector of the test image and of the image in the database is obtained. Second, a similarity measure between these vectors, most often a minimum distance criterion, is used to determine the identity of the face. As pointed out by Brunelli and Poggio, the template based approaches will outperform the early geometric feature based approaches.2.2.Template Based MethodsThe template based approaches represent the most popular technique used to recognize and detect faces. Unlike the geometric feature based approaches, the template based approaches use a feature vector that represent the entire face template rather than the most significant facial features.2.3.Correlation Based MethodsCorrelation based methods for face detection are based on the computation of the normalized cross correlation coefficient Cn. The first step in these methods is to determine the location of the significant facial features such as eyes, nose or mouth. The importance of robust facial feature detection for both detection and recognition has resulted in the development of a variety of different facial feature detection algorithms. The facial feature detection method proposed by Brunelli and Poggio uses a set of templates to detect the position of the eyes in an image, by looking for the maximum absolute values of the normalized correlation coefficient of these templates at each point in test image. To cope with scale variations, a set of templates atdifferent scales was used.The problems associated with the scale variations can be significantly reduced by using hierarchical correlation. For face recognition, the templates corresponding to the significant facial feature of the test images are compared in turn with the corresponding templates of all of the images in the database, returning a vector of matching scores computed through normalized cross correlation. The similarity scores of different features are integrated to obtain a global score that is used for recognition. Other similar method that use correlation or higher order statistics revealed the accuracy of these methods but also their complexity.Beymer extended the correlation based on the approach to a view based approach for recognizing faces under varying orientation, including rotations with respect to the axis perpendicular to the image plane(rotations in image depth). To handle rotations out of the image plane, templates from different views were used. After the pose is determined ,the task of recognition is reduced to the classical correlation method in which the facial feature templates are matched to the corresponding templates of the appropriate view based models using the cross correlation coefficient. However this approach is highly computational expensive, and it is sensitive to lighting conditions.2.4.Matching Pursuit Based MethodsPhilips introduced a template based face detection and recognition system that uses a matching pursuit filter to obtain the face vector. The matching pursuit algorithm applied to an image iteratively selects from a dictionary of basis functions the best decomposition of the image by minimizing the residue of the image in all iterations. The algorithm describes by Philips constructs the best decomposition of a set of images by iteratively optimizing a cost function, which is determined from the residues of the individual images. The dictionary of basis functions used by the author consists of two dimensional wavelets, which gives a better image representation than the PCA (Principal Component Analysis) and LDA(Linear Discriminant Analysis) based techniques where the images were stored as vectors. For recognition the cost function is a measure of distances between faces and is maximized at each iteration. For detection the goal is to find a filter that clusters together in similar templates (themean for example), and minimized in each iteration. The feature represents the average value of the projection of the templates on the selected basis.2.5.Singular Value Decomposition Based MethodsThe face recognition method in this section use the general result stated by the singular value decomposition theorem. Z.Hong revealed the importance of using Singular Value Decomposition Method (SVD) for human face recognition by providing several important properties of the singular values (SV) vector which include: the stability of the SV vector to small perturbations caused by stochastic variation in the intensity image, the proportional variation of the SV vector with the pixel intensities, the variances of the SV feature vector to rotation, translation and mirror transformation. The above properties of the SV vector provide the theoretical basis for using singular values as image features. In addition, it has been shown that compressing the original SV vector into the low dimensional space by means of various mathematical transforms leads to the higher recognition performance. Among the various dimensionality reducing transformations, the Linear Discriminant Transform is the most popular one.2.6.The Dynamic Link Matching MethodsThe above template based matching methods use an Euclidean distance to identify a face in a gallery or to detect a face from a background. A more flexible distance measure that accounts for common facial transformations is the dynamic link introduced by Lades et al. In this approach , a rectangular grid is centered all faces in the gallery. The feature vector is calculated based on Gabor type wavelets, computed at all points of the grid. A new face is identified if the cost function, which is a weighted sum of two terms, is minimized. The first term in the cost function is small when the distance between feature vectors is small and the second term is small when the relative distance between the grid points in the test and the gallery image is preserved. It is the second term of this cost function that gives the “elasticity” of this matching measure. While the grid of the image remains rectangular, the grid that is “best fit” over the test image is stretched. Under certain constraints, until the minimum of the cost function is achieved. The minimum value of the cost function isused further to identify the unknown face.2.7.Illumination Invariant Processing MethodsThe problem of determining functions of an image of an object that are insensitive to illumination changes are considered. An object with Lambertian reflection has no discriminative functions that are invariant to illumination. This result leads the author to adopt a probabilistic approach in which they analytically determine a probability distribution for the image gradient as a function of the surfaces geometry and reflectance. Their distribution reveals that the direction of the image gradient is insensitive to changes in illumination direction. Verify this empirically by constructing a distribution for the image gradient from more than twenty million samples of gradients in a database of thousand two hundred and eighty images of twenty inanimate objects taken under varying lighting conditions. Using this distribution, they develop an illumination insensitive measure of image comparison and test it on the problem of face recognition. In another method, they consider only the set of images of an object under variable illumination, including multiple, extended light sources, shadows, and color. They prove that the set of n-pixel monochrome images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in IR and that the dimension of this illumination cone equals the number of distinct surface normal. Furthermore, the illumination cone can be constructed from as few as three images. In addition, the set of n-pixel images of an object of any shape and with a more general reflectance function, seen under all possible illumination conditions, still forms a convex cone in IRn. These results immediately suggest certain approaches to object recognition. Throughout, they present results demonstrating the illumination cone representation.2.8.Support Vector Machine ApproachFace recognition is a K class problem, where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier, they developed a SVM-based face recognition algorithm. The facerecognition problem is formulated as a problem in difference space, which models dissimilarities between two facial images. In difference space we formulate face recognition as a two class problem. The classes are: dissimilarities between faces of the same person, and dissimilarities between faces of different people. By modifying the interpretation of the decision surface generated by SVM, we generated a similarity metric between faces that are learned from examples of differences between faces. The SVM-based algorithm is compared with a principal component analysis (PCA) based algorithm on a difficult set of images from the FERET database. Performance was measured for both verification and identification scenarios. The identification performance for SVM is 77-78% versus 54% for PCA. For verification, the equal error rate is 7% for SVM and 13% for PCA.2.9.Karhunen- Loeve Expansion Based Methods2.9.1.Eigen Face ApproachIn this approach, face recognition problem is treated as an intrinsically two dimensional recognition problem. The system works by projecting face images which represents the significant variations among known faces. This significant feature is characterized as the Eigen faces. They are actually the eigenvectors. Their goal is to develop a computational model of face recognition that is fact, reasonably simple and accurate in constrained environment. Eigen face approach is motivated by the information theory.2.9.2.Recognition Using Eigen FeaturesWhile the classical eigenface method uses the KLT (Karhunen- Loeve Transform) coefficients of the template corresponding to the whole face image, the author Pentland et.al. introduce a face detection and recognition system that uses the KLT coefficients of the templates corresponding to the significant facial features like eyes, nose and mouth. For each of the facial features, a feature space is built by selecting the most significant “eigenfeatures”, which are the eigenvectors corresponding to the largest eigen values of the features correlation matrix. The significant facial features were detected using the distance from the feature space and selecting the closest match. The scores of similarity between the templates of the test image and thetemplates of the images in the training set were integrated in a cumulative score that measures the distance between the test image and the training images. The method was extended to the detection of features under different viewing geometries by using either a view-based Eigen space or a parametric eigenspace.2.10.Feature Based Methods2.10.1.Kernel Direct Discriminant Analysis AlgorithmThe kernel machine-based Discriminant analysis method deals with the nonlinearity of the face patterns’ distribution. This method also effectively solves the so-called “small sample size” (SSS) problem, which exists in most Face Recognition tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the Generalized Discriminant Analysis (GDA), respectively.2.10.2.Features Extracted from Walshlet PyramidA novel Walshlet Pyramid based face recognition technique used the image feature set extracted from Walshlets applied on the image at various levels of decomposition. Here the image features are extracted by applying Walshlet Pyramid on gray plane (average of red, green and blue. The proposed technique is tested on two image databases having 100 images each. The results show that Walshlet level-4 outperforms other Walshlets and Walsh Transform, because the higher level Walshlets are giving very coarse color-texture features while the lower level Walshlets are representing very fine color-texture features which are less useful to differentiate the images in face recognition.2.10.3.Hybrid Color and Frequency Features ApproachThis correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model(EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQcolor space, which combines the component image R of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.2.10.4.Multilevel Block Truncation Coding ApproachIn Multilevel Block Truncation coding for face recognition uses all four levels of Multilevel Block Truncation Coding for feature vector extraction resulting into four variations of proposed face recognition technique. The experimentation has been conducted on two different face databases. The first one is Face Database which has 1000 face images and the second one is “Our Own Database” which has 1600 face images. To measure the performance of the algorithm the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) parameters have been used. The experimental results have shown that the outcome of BTC (Block truncation Coding) Level 4 is better as compared to the other BTC levels in terms of accuracy, at the cost of increased feature vector size.2.11.Neural Network Based AlgorithmsTemplates have been also used as input to Neural Network (NN) based systems. Lawrence et.al proposed a hybrid neural network approach that combines local image sampling, A self organizing map (SOM) and a convolutional neural network. The SOP provides a set of features that represents a more compact and robust representation of the image samples. These features are then fed into the convolutional neural network. This architecture provides partial invariance to translation, rotation, scale and facedeformation. Along with this the author introduced an efficient probabilistic decision based neural network (PDBNN) for face detection and recognition. The feature vector used consists of intensity and edge values obtained from the facial region of the down sampled image in the training set. The facial region contains the eyes and nose, but excludes the hair and mouth. Two PDBNN were trained with these feature vectors and used one for the face detection and other for the face recognition.2.12.Model Based Methods2.12.1.Hidden Markov Model Based ApproachIn this approach, the author utilizes the face that the most significant facial features of a frontal face which includes hair, forehead, eyes, nose and mouth which occur in a natural order from top to bottom even if the image undergo small variation/rotation in the image plane perpendicular to the image plane. One dimensional HMM (Hidden Markov Model) is used for modeling the image, where the observation vectors are obtained from DCT or KLT coefficients. They given c face images for each subject of the training set, the goal of the training set is to optimize the parameters of the Hidden Markov Model to best describe the observations in the sense of maximizing the probability of the observations given in the model. Recognition is carried out by matching the best test image against each of the trained models. To do this, the image is converted to an observation sequence and then model likelihoods are computed for each face model. The model with the highest likelihood reveals the identity of the unknown face.2.12.2.The Volumetric Frequency Representation of Face ModelA face model that incorporates both the three dimensional (3D) face structure and its two-dimensional representation are explained (face images). This model which represents a volumetric (3D) frequency representation (VFR) of the face , is constructed using range image of a human head. Making use of an extension of the projection Slice Theorem, the Fourier transform of any face image corresponds to a slice in the face VFR. For both pose estimation and face recognition a face image is indexed in the 3D VFR based on the correlation matching in a four dimensional Fourier space, parameterized over the elevation, azimuth, rotation in the image planeand the scale of faces.3.ConclusionThis paper discusses the different approaches which have been employed in automatic face recognition. In the geometrical based methods, the geometrical features are selected and the significant facial features are detected. The correlation based approach needs face template rather than the significant facial features. Singular value vectors and the properties of the SV vector provide the theoretical basis for using singular values as image features. The Karhunen-Loeve expansion works by projecting the face images which represents the significant variations among the known faces. Eigen values and Eigen vectors are involved in extracting the features in KLT. Neural network based approaches are more efficient when it contains no more than a few hundred weights. The Hidden Markov model optimizes the parameters to best describe the observations in the sense of maximizing the probability of observations given in the model .Some methods use the features for classification and few methods uses the distance measure from the nodal points. The drawbacks of the methods are also discussed based on the performance of the algorithms used in the approaches. Hence this will give some idea about the existing methods for automatic face recognition.中文译文人脸识别技术综述摘要人脸是心灵的指标。
人脸信息的重要性英语作文
In todays digital age,the importance of facial information has become increasingly significant.It is a unique identifier that is used in various applications,from unlocking smartphones to accessing secure facilities.Here are some key points to consider when discussing the importance of facial information:1.Identity Verification:Facial recognition technology is used to verify the identity of individuals.This is crucial in sectors such as banking,where it ensures that only the account holder can access their funds.2.Security Enhancement:Facial information is a biometric feature that can be used to enhance security measures.It is harder to forge or replicate than traditional passwords or PINs,making it a more reliable method for access control.3.Efficiency in Services:In customer service industries,facial recognition can streamline processes by quickly identifying returning customers,thus providing personalized service and improving overall efficiency.w Enforcement:Facial information is invaluable in law enforcement for identifying suspects and criminals.It can help in tracking down individuals who are wanted for various crimes,thereby contributing to public safety.5.Healthcare Applications:In the healthcare sector,facial recognition can be used to ensure that patients receive the correct treatment and medication,reducing the risk of errors that could arise from misidentification.6.Social Media Integration:Social media platforms often use facial recognition to suggest tags for photos or to identify users in images,which can enhance user experience by making content more personalized and interactive.7.Privacy Concerns:While facial information is beneficial in many ways,it also raises privacy concerns.The collection and storage of such sensitive data require strict regulations to prevent misuse and protect individuals rights.8.Ethical Considerations:The use of facial information also brings up ethical questions about consent and the potential for discrimination based on appearance or other factors that can be inferred from facial features.9.Technological Advancements:As technology advances,the accuracy and reliability of facial recognition systems improve,making them more useful in a wider range of applications.10.Global Implications:Facial information is not just a domestic concern it has global implications.International travel,for example,increasingly relies on facial recognition for passport control and customs clearance.In conclusion,facial information is a powerful tool in the modern world,offering numerous benefits but also presenting challenges that must be carefully managed to ensure its responsible use.。
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人脸识别论文中英文附录(原文及译文)翻译原文来自Thomas David Heselt ine BSc. Hons. The Un iversity of YorkDepartme nt of Computer Scie neeFor the Qualification of PhD. -- September 2005 -《Face Recog niti on: Two-Dime nsio nal and Three-Dime nsional Tech nique》4 Two-dimensional Face Recognition4.1 Feature LocalizationBefore discuss ing the methods of compari ng two facial images we now take a brief look at some at the prelimi nary processes of facial feature alig nment. This process typically con sists of two stages: face detect ion and eye localisati on. Depe nding on the applicati on, if the positi on of the face with in the image is known beforeha nd (for a cooperative subject in a door access system for example) the n the face detect ion stage can ofte n be skipped, as the regi on of in terest is already known. Therefore, we discuss eye localisati on here, with a brief discussi on of face detect ion in the literature review(sect ion 3.1.1).The eye localisati on method is used to alig n the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are represe ntative of the face recog niti on accuracy and not a product of the performa nee of the eye localisati on rout ine, all image alig nments are manu ally checked and any errors corrected, prior to testi ng and evaluati on.We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of faces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template.Figure 4-1 - The average eyes. Used as a template for eye detection.Both eyes are in cluded in a sin gle template, rather tha n in dividually search ing for each eye in turn, as the characteristic symmetry of the eyes either side of the no se, provides a useful feature that helps disti nguish betwee n the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale(i.e. subject distance from the camera) and also in troduces the assumpti on that eyes in the image appear n ear horiz on tai. Some preliminary experimentation also reveals that it is advantageous to include the area of skin just ben eath the eyes. The reas on being that in some cases the eyebrows can closely match the template, particularly if thereare shadows in the eye-sockets, but the area of skin below the eyes helps to disti nguish the eyes from eyebrows (the area just below the eyebrows con tai n eyes, whereas the area below the eyes contains only plain skin).A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the image with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smaller template of the in dividual left and right eyes the n refi nes each eye positi on.This basic template-based method of eye localisati on, although provid ing fairly preciselocalisati ons, ofte n fails to locate the eyes completely. However, we are able to improve performa nce by in cludi ng a weighti ng scheme.Eye localisati on is performed on the set of training images, which is the n separated in to two sets: those in which eye detect ion was successful; and those in which eye detect ion failed. Taking the set of successful localisatio ns we compute the average dista nce from the eye template (Figure 4-2 top). Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as we would expect. However, bright points do occur near the whites of the eye, suggesting that this area is often inconsistent, varying greatly from the average eye template.Figure 4-2 -Distance to the eye template for successful detections (top) indicating variance due to noise and failed detections (bottom) showing credible variance due to miss-detected features.In the lower image (Figure 4-2 bottom), we have take n the set of failed localisati on s(images of the forehead, no se, cheeks, backgro und etc. falsely detected by the localisati on routi ne) and once aga in computed the average dista nce from the eye template. The bright pupils surr oun ded by darker areas in dicate that a failed match is ofte n due to the high correlati on of the nose and cheekb one regi ons overwhel ming the poorly correlated pupils. Wanting to emphasise the differenee of the pupil regions for these failed matches and minimise the varianee of the whites of the eyes for successful matches, we divide the lower image values by the upper image to produce a weights vector as show n in Figure 4-3. When applied to the differe nee image before summi ng a total error, this weight ing scheme provides a much improved detect ion rate.Figure 4-3 - Eye template weights used to give higher priority to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begi n our inv estigatio n into face recog niti on with perhaps the simplest approach,k nown as the direct correlation method (also referred to as template matching by Brunelli and Poggio [29 ]) inv olvi ng the direct comparis on of pixel inten sity values take n from facial images. We use the term ‘ Direct Correlation ' to encompass all techniques in which face images are compareddirectly, without any form of image space an alysis, weight ing schemes or feature extracti on, regardless of the dsta nee metric used. Therefore, we do not infer that Pears on ' s correlat applied as the similarity fun cti on (although such an approach would obviously come un der our definition of direct correlation). We typically use the Euclidean distance as our metric in these inv estigati ons (in versely related to Pears on ' s correlati on and can be con sidered as a scale tran slati on sen sitive form of image correlati on), as this persists with the con trast made betwee n image space and subspace approaches in later sect ions.Firstly, all facial images must be alig ned such that the eye cen tres are located at two specified pixel coord in ates and the image cropped to remove any backgro und in formati on. These images are stored as greyscale bitmaps of 65 by 82 pixels and prior to recog niti on con verted into a vector of 5330 eleme nts (each eleme nt containing the corresp onding pixel inten sity value). Each corresp onding vector can be thought of as describ ing a point with in a 5330 dime nsional image space. This simple prin ciple can easily be exte nded to much larger images: a 256 by 256 pixel image occupies a si ngle point in 65,536-dime nsional image space and again, similar images occupy close points within that space. Likewise, similar faces are located close together within the image space, while dissimilar faces are spaced far apart. Calculati ng the Euclidea n dista need, betwee n two facial image vectors (ofte n referred to as the query image q, and gallery imageg), we get an indication of similarity. A threshold is then applied to make the final verification decision.d q g (d threshold ? accept) d threshold ? reject ) . Equ. 4-14.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify aclaimed identity or determine a person's most likely identity from a set of potential matches in a database. In order to assess a given system ' s ability to perform these tasks, a variety of evaluati on methodologies have arise n. Some of these an alysis methods simulate a specific mode of operatio n (i.e. secure site access or surveilla nee), while others provide a more mathematical description of data distribution in some classificatio n space. In additi on, the results gen erated from each an alysis method may be prese nted in a variety of formats. Throughout the experime ntatio ns in this thesis, weprimarily use the verification test as our method of analysis and comparison, although we also use Fisher Lin ear Discrim inant to an alyse in dividual subspace comp onents in secti on 7 and the iden tificati on test for the final evaluatio ns described in sect ion 8. The verificati on test measures a system ' s ability to correctly accept or reject the proposed ide ntity of an in dividual. At a fun cti on al level, this reduces to two images being prese nted for comparis on, for which the system must return either an accepta nee (the two images are of the same pers on) or rejectio n (the two images are of differe nt people). The test is desig ned to simulate the applicati on area of secure site access. In this scenario, a subject will present some form of identification at a point of en try, perhaps as a swipe card, proximity chip or PIN nu mber. This nu mber is the n used to retrieve a stored image from a database of known subjects (ofte n referred to as the target or gallery image) and compared with a live image captured at the point of entry (the query image). Access is the n gran ted depe nding on the accepta nce/rejecti on decisi on.The results of the test are calculated accord ing to how many times the accept/reject decisi on is made correctly. In order to execute this test we must first define our test set of face images. Although the nu mber of images in the test set does not affect the results produced (as the error rates are specified as percentages of image comparisons), it is important to ensure that the test set is sufficie ntly large such that statistical ano malies become in sig ni fica nt (for example, a couple of badly aligned images matching well). Also, the type of images (high variation in lighting, partial occlusions etc.) will significantly alter the results of the test. Therefore, in order to compare multiple face recog niti on systems, they must be applied to the same test set.However, it should also be no ted that if the results are to be represe ntative of system performance in a real world situation, then the test data should be captured under precisely the same circumsta nces as in the applicati on en vir onmen t. On the other han d, if the purpose of the experime ntati on is to evaluate and improve a method of face recog niti on, which may be applied to a range of applicati on en vir onmen ts, the n the test data should prese nt the range of difficulties that are to be overcome. This may mea n in cludi ng a greater perce ntage of ‘ difficult would be expected in the perceived operati ng con diti ons and hence higher error rates in the results produced. Below we provide the algorithm for execut ing the verificati on test. The algorithm is applied to a sin gle test set of face images, using a sin gle fun cti on call to the face recog niti on algorithm: CompareFaces(FaceA, FaceB). This call is used to compare two facial images, returni ng a dista nce score in dicat ing how dissimilar the two face images are: the lower the score the more similar the two face images. Ideally, images of the same face should produce low scores, while images of differe nt faces should produce high scores.Every image is compared with every other image, no image is compared with itself and no pair is compared more tha n once (we assume that the relati on ship is symmetrical). Once two images have been compared, producing a similarity score, the ground-truth is used to determine if the images are ofthe same person or different people. In practical tests this information is ofte n en capsulated as part of the image file name (by means of a unique pers on ide ntifier). Scores are the n stored in one of two lists: a list containing scores produced by compari ng images of differe nt people and a list containing scores produced by compari ng images of the same pers on. The final accepta nce/reject ion decisi on is made by applicati on of a threshold. Any in correct decision is recorded as either a false acceptance or false rejection. The false rejection rate (FRR) is calculated as the perce ntage of scores from the same people that were classified as rejectio ns. The false accepta nce rate (FAR) is calculated as the perce ntage of scores from differe nt people that were classified as accepta nces.For IndexA = 0 to length (TestSet)For IndexB = lndexA+1 to length (T estSet)Score = CompareFaces (T estSet[IndexA], TestSet[IndexB]) If IndexA and IndexB are the same person Append Score to AcceptScoresListElseAppend Score to RejectScoresListFor Threshold = Minimum Score to Maximum Score:FalseAcceptCount, FalseRejectCount = 0For each Score in RejectScoresListIf Score <= ThresholdIncrease FalseAcceptCountFor each Score in AcceptScoresListIf Score > ThresholdIncrease FalseRejectCountFalseAcceptRate = FalseAcceptCount / Length(AcceptScoresList) FalseRejectRate = FalseRejectCount / length(RejectScoresList) Add plot to error curve at (FalseRejectRate, FalseAcceptRate)These two error rates express the in adequacies of the system whe n operat ing at aspecific threshold value. Ideally, both these figures should be zero, but in reality reducing either the FAR or FRR (by alteri ng the threshold value) will in evitably resultin increasing the other. Therefore, in order to describe the full operating range of a particular system, we vary the threshold value through the en tire range of scores produced. The applicati on of each threshold value produces an additi onal FAR, FRR pair, which when plotted on a graph produces the error rate curve shown below.Figure 4-5 - Example Error Rate Curve produced by the verification test.The equal error rate (EER) can be see n as the point at which FAR is equal to FRR. This EER value is often used as a single figure representing the general recognition performa nee of a biometric system and allows for easy visual comparis on of multiple methods. However, it is important to note that the EER does not indicate the level of error that would be expected in a real world applicati on .It is un likely that any real system would use a threshold value such that the perce ntage of false accepta nces were equal to the perce ntage of false rejecti ons. Secure site access systems would typically set the threshold such that false accepta nces were sig nifica ntly lower tha n false rejecti ons: unwilling to tolerate intruders at the cost of inconvenient access denials.Surveilla nee systems on the other hand would require low false rejectio n rates to successfully ide ntify people in a less con trolled en vir onment. Therefore we should bear in mind that a system with a lower EER might not n ecessarily be the better performer towards the extremes of its operating capability.There is a strong conn ecti on betwee n the above graph and the receiver operat ing characteristic (ROC) curves, also used in such experime nts. Both graphs are simply two visualisati ons of the same results, in that the ROC format uses the True Accepta nee Rate(TAR), where TAR = 1.0 -FRR in place of the FRR, effectively flipping the graph vertically. Another visualisation of the verification test results is to display both the FRR and FAR as functions of the threshold value. This prese ntati on format provides a refere nee to determ ine the threshold value necessary to achieve a specific FRR and FAR. The EER can be seen as the point where the two curves in tersect.ThrasholdFigure 4-6 - Example error rate curve as a function of the score thresholdThe fluctuati on of these error curves due to no ise and other errors is depe ndant on the nu mber of face image comparis ons made to gen erate the data. A small dataset that on ly allows for a small nu mber of comparis ons will results in a jagged curve, in which large steps corresp ond to the in flue nce of a si ngle image on a high proporti on of thecomparis ons made. A typical dataset of 720 images (as used in sect ion 422) provides 258,840 verificatio n operati ons, hence a drop of 1% EER represe nts an additi onal 2588 correct decisions, whereas the quality of a single image could cause the EER tofluctuate by up to 0.28.4.2.2 ResultsAs a simple experiment to test the direct correlation method, we apply the technique described above to a test set of 720 images of 60 different people, taken from the AR Face Database [ 39 ]. Every image is compared with every other image in the test set to produce a like ness score, provid ing 258,840 verificati on operati ons from which to calculate false accepta nce rates and false rejecti on rates. The error curve produced is show n in Figure 4-7.Figure 4-7 - Error rate curve produced by the direct correlation method using no image preprocessing.We see that an EER of 25.1% is produced, meaning that at the EER thresholdapproximately one quarter of all verification operations carried out resulted in anin correct classificati on. There are a nu mber of well-k nown reas ons for this poor levelof accuracy. Tiny changes in lighting, expression or head orientation cause the location in image space to cha nge dramatically. Images in face space are moved far apart due to these image capture conditions, despite being of the same person ' s face. The distanee between images differe nt people becomes smaller tha n the area of face space covered by images of the same pers on and hence false accepta nces and false rejecti ons occur freque ntly. Other disadva ntages in clude the large amount of storage n ecessary for holdi ng many face images and the inten sive process ing required for each comparis on, making this method un suitable for applicati ons applied to a large database. In secti on 4.3 we explore the eige nface method, which attempts to address some of these issues.4二维人脸识别4.1功能定位在讨论比较两个人脸图像，我们现在就简要介绍的方法一些在人脸特征的初步调整过程。