Facial Recognition--人脸识别
face_recognition人脸识别模块
face_recognition⼈脸识别模块face_recognition ⼈脸识别模块（安装之前，必须先安装dlib库）1. 基于dlib库，进⾏了⼆次封装。
号称是世界上最简洁的⼈脸识别库。
2. 训练数据集：是⿇省理⼯⼤学下属的学院利⽤13000多张照⽚为基础。
主要是以欧美⼈为主。
在 command line下安装模块时：F:\Python_AI\venv\Scripts> pip install face_recoginitonload_image_file : 加载要识别的⼈脸图像，返回Numpy数组，返回的是图⽚像素的特征向量（⼤⼩，⽅向）face_loctions: 定位图中⼈脸的坐标位置。
返回矩形框的location（top,right,bottom,left）face_landmarks: 识别⼈脸的特征点; 返回68个特征点的坐标位置（chin....）face_encodings: 获取图像中所有⼈脸的编码信息（向量）compare_faces: 由⾯部编码信息进⾏⾯部识别匹配。
#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDrawimport cv2# face_image = face_recognition.load_image_file('imgs/twis001.jpg')face_image = cv2.imread('imgs/twis001.jpg')face_marks_list = face_recognition.face_landmarks(face_image)pil_image = Image.fromarray(face_image)d = ImageDraw.Draw(pil_image)for face_marks in face_marks_list:facial_features = ['chin','left_eyebrow','right_eyebrow','nose_bridge','nose_tip','left_eye','right_eye','bottom_lip']for facial_feature in facial_features:# print("{}: 特征位置：{}".format(facial_feature, face_marks[facial_feature]))# d.line(face_marks[facial_feature], fill=(0, 255, 0), width=5)# d.point(face_marks[facial_feature], fill=(255, 0, 0))for p in face_marks[facial_feature]:print(p)cv2.circle(face_image, p, 1, (0, 255, 0), 1)cv2.imshow("images", face_image)cv2.imwrite("twis01.jpg",face_image)cv2.waitKey(0)cv2.destroyAllWindows()# pil_image.show()#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDraw, ImageFontimage_known = face_recognition.load_image_file('imgs/yangmi/yangmidanr00.jpg')image_unknown = face_recognition.load_image_file('imgs/yangmi/yangmihe02.jpg')image_known_encodings = face_recognition.face_encodings(image_known)[0]face_locations = face_recognition.face_locations(image_unknown)results = []for i in range(len(face_locations)):top, right, bottom, left = face_locations[i]face_image = image_unknown[top:bottom, left:right]face_encoding = face_recognition.face_encodings(face_image)if face_encoding:result = {}matches = face_pare_faces(face_encoding, image_known_encodings, tolerance=0.5) if True in matches:print('在未知图⽚中找到了已知⾯孔')result['face_encoding'] = face_encodingresult['is_view'] = Trueresult['location'] = face_locations[i]result['face_id'] = i + 1results.append(result)if result['is_view']:print("已知⾯孔匹配照⽚上的第{}张脸！".format(result['face_id']))pil_image = Image.fromarray(image_unknown)draw = ImageDraw.Draw(pil_image)view_face_locations = [i['location'] for i in results if i['is_view']]for location in view_face_locations:top, right, bottom, left = locationdraw.rectangle([(left, top), (right, bottom)], outline=(0, 255, 0), width=2)font = ImageFont.truetype("consola.ttf", 20, encoding='unic')draw.text((left, top - 20), "yangmi", (255, 0, 0), font=font)pil_image.show()# 可以试着⽤cv2来画框，和写字 puttext#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionimport cv2img_known = face_recognition.load_image_file("imgs/joedan/cows.jpeg")img_unkown = face_recognition.load_image_file("imgs/joedan/joedan01.jpg")face_encodings_known = face_recognition.face_encodings(img_known)face_encodings_unknow = face_recognition.face_encodings(img_unkown)[0]matches = face_pare_faces(face_encodings_known, face_encodings_unknow, tolerance=0.5) print(matches)locations = face_recognition.face_locations(img_known)print(locations)if True in matches:index = matches.index(True)match = locations[index]print(match)top, right, bottom, left = matchcv2.rectangle(img_known, (left, top), (right, bottom), (0, 0, 255), 2)cv2.imshow("images", img_known)cv2.waitKey(0)cv2.destroyAllWindows()。
facial recognition阅读理解
facial recognition阅读理解一、简介Facial Recognition是一种基于人工智能的技术，能够识别和分析图像中的面孔，从而对目标对象进行身份验证和定位。
在本文中，我们将讨论这项技术的基本概念、应用场景、优缺点以及相关法律法规。
二、基本原理Facial Recognition的核心原理是基于图像处理和机器学习算法。
它首先对输入的图像进行预处理，如光线调整、面部对齐等，以改善识别精度。
然后，使用机器学习算法训练一个模型，该模型能够从图像中提取面部的特征，并与数据库中的已知面孔进行比对。
如果匹配成功，则识别成功。
三、应用场景1. 安全监控：在公共场所，如机场、车站、大型活动现场等，使用Facial Recognition技术可以进行人员身份验证和流量控制，提高安全保障。
2. 人脸识别门禁：在住宅小区、办公大楼等场所，Facial Recognition门禁系统可以方便快捷地验证访客身份，提高门禁管理效率。
3. 社交媒体应用：许多社交媒体应用使用Facial Recognition技术来识别用户上传的照片中的人物，并提供相关标签和推荐。
4. 执法机构：在刑事调查和人口统计中，Facial Recognition也发挥着重要作用。
四、优缺点优点：1. 准确度高：随着技术的进步，Facial Recognition的准确度越来越高。
2. 快速高效：相对于传统的人工比对方法，Facial Recognition可以大大提高识别的速度和效率。
3. 非接触式：Facial Recognition系统通常是非接触式的，对用户来说更加方便和舒适。
缺点：1. 隐私问题：Facial Recognition涉及个人隐私，需要遵守相关的法律法规。
2. 技术局限性：Facial Recognition技术在一些特殊情况下（如化妆、发型改变、面部遮挡等）可能无法正常工作。
3. 安全风险：如果系统被黑客攻击或滥用来识别无辜公民，可能会引发安全风险。
【中考英语总复习 考点专项练习】39 阅读理解之说明文(原卷及答案)
专项39 阅读理解之说明文＂英语说明文＂，顾名思义，就是一种以＂说明、解释＂为主要表达方式的英语文体。
它是对客观事物的性状、特点、功能和用途等作科学解说的。
它既不像故事那样重在情节的叙述和描写，也不像议论文那样，重在阐明主张和论点论据；更不像科幻作品那样富于想象和虚构夸张。
说明文是通过解说事物、阐明事理，使人们增长知识和技能。
说明文是高考英语阅读理解题中的重点内容之一。
说明文具有与自己特点相适应的说明方法，因此说明文结构复杂，专业术语多，易于拉开考生分数档次，便于高校分层次选拔人才。
然而对于考生来说说明文抽象度高，解题难度大。
高考对说明文的考查多为科普知识，动植物特性、自然现象和新产品、新工艺介绍以及人文地理、风土人情等方面的说明文，文中解释性、定义性、说明性的句子居多。
因此考生要掌握说明文的命题特点，叙述方式，以冷静的心态阅读原文，重点突破长句结构特点和逻辑关系，以便对其做出准确的语意理解。
说明文阅读理解的特征一般说来，英语说明文与其他文体一样，文章所涉及的内容不外乎以下几个方面，即who → what → when → where → how → why。
1. who：问的是这篇文章的主体是谁？（即所要说明和描述的人或事物）2. what：问的是主体做了什么事情？（即主体表现出的特性、功能和用途）3. when和where：是在何时何地发生的？（即何时何地所表现出的特性、功能和用途）4. how：通过什么方式表现出来的？5. why：这种特性功能用途的原因是什么？做说明文阅读理解题的时候，一定要记住上面的wh-word。
边阅读，边搜记，牢记要点，把握全文。
A（2022·西藏·中考真题）The Fuxing train has started running in southwest China’s Tibet along the Lhasa—Nyingchi Railway(拉萨—林芝铁路).The 435-kilometer railway links Lhasa and Nyingchi. It is China’s first electrified(电气化的) railroad on the “Roof of the World”. It shortens the journey between Lhasa and Nyingchi fromover five hours to about 3.5 hours.The new railway covers an area of 226,000 square kilometers. It goes through 47 tunnels (隧道) and crosses 121 bridges. The train has a top speed of 160 kilometers per hour. It is the fastest train on the high land, but it’s a bit slower than some other Fuxing trains in our country because of the special environment. The railway not only makes local people’s life easier, but also brings more tourists to the area.“The railway trip is so relaxing and fast. The new train has brought our family members closer than ever before,” said a teacher who lives in Nyingchi. She used to drive more than five hours from Nyingchi to Lhasa to visit her parents during the summer vacation.Besides carrying passengers, the railway also provides good chances for farmers to sell local products to more places. It’s sure to put new energy into Tibet’s economic(经济的) development. 1．How many hours does the journey take between Lhasa and Nyingchi by Fuxing train? A．About 5 hours.B．About 4 hours.C．About 3.5 hours.D．About 1.5 hours. 2．What’s the top speed of Fuxing train from Lhasa to Nyingchi?A．160 kilometers per hour.B．121 kilometers per hour.C．47 kilometers per hour.D．160 meters per hour.3．What does the underlined word “shortens” mean in Chinese?A．克扣B．下降C．缩短D．缺少4．What’s special about the Lhasa—Nyingchi Railway?A．It covers a large area.B．It can carry passengers.C．The train goes through lots of tunnels and bridges.D．It’s China’s first electrified railway on the “Roof of the World”.5．What can we learn from the passage?A．The railway trip is not relaxing or fast.B．The railway does not bring more tourists to the area.C．The train goes through 121 tunnels and 47 bridges.D．It’s a bit slower than some other Fuxing trains in our country.B（2022·江苏南京·中考真题）Sue and Jim were neighbours for five years but they were never interested in each other. Then one day, Sue saw Jim’s music collection. She noticed a rare punk rock CD that she also owned. At that moment, she realized that they both had the same interest in music and they started talking. Sue said, “I thought we had nothing in common (相同) until I saw his CD by the New York Dolls.” They are now married and living with each other.Some psychologists (心理学家) think that your taste in music is related to (有联系的) your personality. As part of a test at the University of Texas, Austin, USA, volunteers created a CD of their favourite songs. The volunteers then listened to each other’s CDs and made guesses about the CD creator’s personality—outgoing, adventurous, happy, and so on. These strangers correctly guessed much more about each others’ personalities through their CDs than through their clothesor taste in films. For example, Sue and Jim love punk music, which means they have outgoing personalities.The psychologists who did the test found Snoop Dogg (hip-hop) fans are likely to be energetic and talkative. People who like U2 (rock/pop music) are generally independent and adventurous.The psychologists also found that Louis Armstrong (jazz) fans tend to (倾向于) be serious and intelligent. While fans of classical music are also likely to enjoy jazz music and tend to be shy. The psychologists were surprised to find that rap and heavy metal fans were also shier and quieter than many other music lovers.C（2022·重庆璧山·一模）Pollution inside homes and other buildings kills more than 4 million people each year. Many people die as a result of breathing smoke or from cooking over wood powered or coal powered stoves.One way to reduce the number of deaths is through cooking equipment (设备) powered by the sun. Crosby Menzies, a solar power expert in a South African company, described his latest solar cooker called the “Sol 4”. “It is four square meters of mirrors, six to eight meters in length. It is quite a large cooker.”The “Sol 4” works by reflecting (反射) light from the sun off large mirrors. The mirrors direct (把……对准) the light at a cooking pan. Then the light heats the pan with solar energy. Within two minutes, the pan is hot enough to cook sausages and onions. In just four minutes, water can be boiled. That is as fast as cooking with natural gas or electricity.The cooker is also much “friendlier” than other models because people do not have to stand in the sun to use it. And people can prepare meals without having to collect firewood or spend money on coal. Such individuals would be less likely to have breathing illnesses from harmful smoke.At present, the cooker only works when the sun is shining. But engineers are working on a way to make it work without sunlight.Each cooker costs about $2,000, which is a large amount of money for most Africans. To solve that problem, Crosby Menzies has created a nongovernmental (非官方) organization called Solar Cookers for Africa. It plans to raise money through donations (捐款) to provide poor people with solar cooking equipment.16．The writer starts the passage by ________.A．comparing facts B．sharing experiences C．listing numbers D．giving reasons 7．The underlined word “individuals” most probably means “________”.A．cookers B．people C．mirrors D．experts 18．Which of the following about the “Sol 4” is TRUE ?A．It is six to eight square meters of mirrors.B．It can work in any weather condition now.C．It can also heat the pan by lighting firewood.D．It is more environment-friendly than other cookers.19．From the passage, we can infer that ________.A．the “Sol 4” will be designed in a smaller sizeB．more Africans can use the solar cookers in the futureC．the “Sol 4” is the first product of the South African companyD．the African government will provide poor people with solar cookerD（2022·重庆开州·二模）◆Facial-recognition(人脸识别) cameras are everywhere, even in your smartphone. Many people use this technology to unlock their phones, open doors, or make quick payments. But during the pandemic(疫情), can it still work when people put on masks?◆The answer is no. Scientists are trying to solve this problem. For example, Engineering & Technology(E＆T) magazine reports that Hanwang, a tech company in Beijing, has made a new system(系统). It can recognize 95 percent of people when they wear masks.◆What’s the secret? It’s all about your eyes.◆Marios Savides from Carnegie Mellon University, US, studies facial recognition. As we get older, he says, our faces change shape. But the area around our eyes doesn’t. It stays the same—even if we put on weight. As long as there are enough data(数据) from the area of our eyes, the cameras can recognize us.◆Tech 5 is another facial-recognition company. The Swiss company’s product scans(扫描) the shape of your face. It also scans your iris(虹膜), which is the colored part of your eyes. Tech 5 hopes to leave out(忽略) all of the face below the nose.◆This kind of software is more important than just unlocking your phone. “It can search for criminals(罪犯) and terrorists(恐怖分子) even when they wear masks,” said Huang-Lei from Hanwang. Hanwang’s technology is used by Chinese police in places like subway stations and airports. Similar technology is used by the United States Air Force.◆Huang pointed out one big weakness: it fails when people wear both a mask and sunglasses. “In this situation, all of the key facial information is lost,” Lei said to E&T.20．The writer leads to the topic(话题) by ________.A．giving an example B．telling stories C．listing numbers D．raising a problem 21．The new system can recognize 95 percent people by scanning ________.A．the face B．the eyes C．the nose D．the mouth22．We can infer that the new system ________.A．can recognize all people with masks in the futureB．can help the police to look for criminalsC．has brought plenty of money to Tech 5D．can recognize a person even when he loses some weight 23．The best structure of this passage is ________.A．B．C．D．专项39 阅读理解之说明文＂英语说明文＂，顾名思义，就是一种以＂说明、解释＂为主要表达方式的英语文体。
100多个常用英语转化词
100多个常用英语转化词以下是100多个常用的英语转化词：1. 在家工作– work from home2. 线上学习– online learning3. 远程会议– remote meeting4. 社交距离– social distancing5. 个人防护装备– personal protective equipment (PPE)6. 新冠病毒– COVID-197. 疫苗接种– vaccine administration8. 医疗保健工作者– healthcare worker9. 感染控制– infection control10. 新常态– new normal11. 数字化转型– digital transformation12. 微信支付– WeChat Pay13. 移动支付– mobile payment14. 在线购物– online shopping15. 无现金支付– cashless payment16. 虚拟现实– virtual reality (VR)17. 增强现实– augmented reality (AR)18. 人工智能– artificial intelligence (AI)19. 云计算– cloud computing20. 物联网– Internet of Things (IoT)21. 网络安全– cyber security22. 数据隐私– data privacy23. 社交媒体– social media24. 数字营销– digital marketing25. 网络营销– online marketing26. 电子商务– e-commerce27. 可持续发展– sustainable development28. 绿色能源– green energy29. 循环经济– circular economy30. 生态友好– eco-friendly31. 大数据– big data32. 云存储– cloud storage33. 人脸识别– facial recognition34. 身份验证– identity verification35. 数据分析– data analysis36. 人工智能助手– AI assistant37. 虚拟助手– virtual assistant38. 智能家居– smart home39. 智能城市– smart city40. 智能交通– smart transportation41. 电子健康记录– electronic health record (EHR)42. 电子钱包– e-wallet43. 远程办公– remote work44. 在线协作– online collaboration45. 云会议– cloud meeting46. 数字签名– digital signature47. 电子签名– electronic signature48. 数字化学习– digital learning49. 虚拟学习– virtual learning50. 宽带互联网– broadband internet51. 无线网络– wireless network52. 电子邮件– email53. 动态驱动器– flash drive54. 数字相机– digital camera55. 蓝牙耳机– Bluetooth headphones56. 触屏设备– touch screen device57. 可穿戴设备– wearable device58. 视频会议– video conference59. 在线支付– online payment60. 科技创新– technological innovation61. 数据保护– data protection62. 电子商务平台– e-commerce platform63. 电子书籍– e-book64. 在线娱乐– online entertainment65. 人脑计算– brain-computer interface (BCI)66. 无人驾驶– autonomous driving67. 3D打印– 3D printing68. 虚拟货币– virtual currency69. 科技公司– technology company70. 网红– internet celebrity71. 视频分享– video sharing72. 在线音乐– online music73. 实时定位– real-time location74. 网购– online shopping75. 电子游戏– video game76. 自助服务– self-service77. 远程医疗– telemedicine78. 电子票务– e-ticketing79. 无线充电– wireless charging80. 电动汽车– electric vehicle (EV)81. 生物识别– biometric identification82. 智能手表– smartwatch83. 无人机– drone84. 虚拟帮手– virtual assistant85. 数码产品– digital products86. 国际化– internationalization87. 数据中心– data center88. 电子证照– electronic license89. 智能监控– intelligent surveillance90. 电子支付– electronic payment91. 电子商务平台– e-commerce platform92. 电子合同– electronic contract93. 数字货币– digital currency94. 无线耳机– wireless earphones95. 高科技产业– high-tech industry96. 电子竞技– e-sports97. 移动应用– mobile application98. 虚拟私人网络– virtual private network (VPN)99. 电子货币– e-currency100. 虚拟课堂– virtual classroom希望对你有帮助！。
Python使用face_recognition人脸识别
Python使⽤face_recognition⼈脸识别Python 使⽤ face_recognition ⼈脸识别官⽅说明：⼈脸识别 face_recognition 是世界上最简单的⼈脸识别库。
使⽤ dlib 最先进的⼈脸识别功能构建建⽴深度学习，该模型准确率在99.38%。
Python模块的使⽤ Python可以安装导⼊ face_recognition 模块轻松操作，对于简单的⼏⾏代码来讲，再简单不过了。
Python操作 face_recognition API ⽂档：⾃动查找图⽚中的所有⾯部import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_locations = face_recognition.face_locations(image)# face_locations is now an array listing the co-ordinates of each face!还可以选择更准确的给予深度学习的⼈脸检测模型import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_locations = face_recognition.face_locations(image, model="cnn")# face_locations is now an array listing the co-ordinates of each face!⾃动定位图像中⼈物的⾯部特征import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_landmarks_list = face_recognition.face_landmarks(image)# face_landmarks_list is now an array with the locations of each facial feature in each face.# face_landmarks_list[0]['left_eye'] would be the location and outline of the first person's left eye.识别图像中的⾯部并识别它们是谁import face_recognitionpicture_of_me = face_recognition.load_image_file("me.jpg")my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]# my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face! unknown_picture = face_recognition.load_image_file("unknown.jpg")unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]# Now we can see the two face encodings are of the same person with `compare_faces`!results = face_pare_faces([my_face_encoding], unknown_face_encoding)if results[0] == True:print("It's a picture of me!")else:print("It's not a picture of me!")face_recognition ⽤法要在项⽬中使⽤⾯部识别，⾸先导⼊⾯部识别库，没有则安装：import face_recognition基本思路是⾸先加載圖⽚：# 导⼊⼈脸识别库import face_recognition# 加载图⽚image = face_recognition.load_image_file("1.jpg")上⾯这⼀步会将图像加载到 numpy 数组中，如果已经有⼀个 numpy 数组图像则可以跳过此步骤。
人脸识别的基本方法
人脸识别的基本方法
一、人脸识别的基本原理
人脸识别（facial recognition）技术是一种生物识别技术，它可以
作为一种安全认证技术，通过通过对比个人的脸部特征和记录的脸部特征
进行鉴别的方式来确认个人身份，将真实的人脸和图像中的人脸进行对比
来达到鉴别此人的目的。
人脸识别技术的工作原理大致分为三个步骤：
1.特征提取：提取人脸图像的特征，这些特征包括脸型特征、眼睛特征、嘴巴特征、鼻子特征等；
2.特征向量化：将这些特征信息转换成特征向量，以便进行后续比对；
3.比对验证：把已经录入的特征向量和新输入的特征向量进行比较，
验证是否为同一个人的脸部特征。
1.基于模板的匹配方法
这是最常用的人脸识别方法，也是最常用的 biometric 系统之一、
这种方法的核心在于，在认证的过程中，将人脸信息预先存储在数据库中，然后将用户输入的人脸信息和数据库中已存储的信息进行匹配，通过比较
匹配度来确定这是否是同一个人，从而判断用户的身份。
2.基于深度学习的识别方法
在这种方法中，人脸识别系统首先会提取人脸信息，然后利用深度学
习技术，将提取的信息分析出脸部的特征数据，最后将这些特征进行比较，从而判断是否为同一个人。
人脸识别FR
人脸识别FR人脸识别（Facial Recognition）首章：引言人类作为社交动物，面对面的交流在日常生活中扮演着重要的角色。
人脸识别（Facial Recognition）技术的发展为我们带来了便利和安全性的提升。
本文将就人脸识别技术的原理、应用以及相关议题进行探讨。
第二章：人脸识别的基本原理1. 图像采集：人脸识别的第一步是通过摄像设备采集人脸图像。
摄像设备可以是监控摄像头、手机相机或者专用的人脸采集设备。
2. 预处理：为了提高人脸识别的准确性，采集到的图像需要进行预处理。
这包括人脸检测、图像的归一化和去噪等步骤。
3. 特征提取：通过一系列的数学算法，从预处理后的图像中提取人脸的特征。
这些特征可能包括脸部的轮廓、眼睛、嘴巴等特征点的位置和形状。
4. 特征匹配：将提取到的人脸特征与已有的人脸数据库进行比对，寻找与之最接近的人脸。
匹配的方法可以是基于相似度比较的算法，如欧几里德距离或者余弦相似度等。
第三章：人脸识别的应用人脸识别技术的广泛应用使得我们的生活变得更加便利和安全。
以下是几个常见的应用领域：1. 解锁手机：现代智能手机广泛应用了人脸识别技术，使得用户可以通过简单的自拍动作快速解锁手机。
2. 支付验证：一些移动支付平台也开始采用人脸识别技术进行用户身份验证，提高支付的安全性。
3. 身份识别：在边境检查、机场安检、银行业务等环节中，人脸识别技术能够快速准确地识别身份，提高安全性并节省时间。
4. 犯罪抓捕：警方可以通过监控摄像头中的人脸识别技术，快速锁定嫌疑人，并进行追踪和抓捕。
第四章：人脸识别的挑战和争议虽然人脸识别技术在许多领域得到了广泛应用，但也存在一些挑战和争议。
主要包括：1. 隐私问题：人脸识别技术需要采集用户的个人生物信息，这引发了对个人隐私保护的担忧，并引发了隐私法律的争议。
2. 误识别问题：人脸识别技术在特定情况下可能会出现误识别现象，例如在光线不好或者穿戴口罩时。
这可能导致无辜人士受到错误的定罪。
酒店入住人脸识别登记流程
酒店入住人脸识别登记流程英文回答：Hotel Guest Registration with Facial Recognition.Facial recognition technology is becoming increasingly common in the hospitality industry, as hotels seek to improve security and efficiency. By using facial recognition, hotels can streamline the guest registration process, making it faster and more convenient for guests.Benefits of Facial Recognition for Hotel Guest Registration.There are a number of benefits to using facial recognition for hotel guest registration, including:Speed and efficiency: Facial recognition can significantly reduce the time it takes to register a guest. This is because guests do not need to provide theirpersonal information or fumble with a key card.Security: Facial recognition is a highly secure way to verify a guest's identity. This is because facialrecognition systems are very accurate and difficult to fool.Convenience: Facial recognition is a very convenient way for guests to register. This is because guests do not need to remember their room number or carry around a key card.How Facial Recognition Works for Hotel Guest Registration.Facial recognition systems work by comparing a guest's face to a database of known faces. When a guest arrives ata hotel, they will be asked to stand in front of a camera. The camera will take a picture of the guest's face and compare it to the database. If the guest's face matches a face in the database, they will be allowed to register.Implementation of Facial Recognition for Hotel GuestRegistration.Hotels can implement facial recognition for guest registration by following these steps:1. Purchase a facial recognition system: There are a number of different facial recognition systems available on the market. Hotels should research different systems tofind one that meets their needs.2. Install the facial recognition system: The facial recognition system should be installed in a secure location at the hotel.3. Train the facial recognition system: The facial recognition system must be trained with a database of known faces. Hotels can collect this database from their existing guests or from a third-party provider.4. Test the facial recognition system: The facial recognition system should be tested before it is put into use. This will ensure that the system is working properly.Conclusion.Facial recognition is a valuable tool that can help hotels improve security, efficiency, and convenience. By using facial recognition for guest registration, hotels can make the check-in process faster, more secure, and more convenient for guests.中文回答：酒店入住人脸识别登记流程。
介绍人脸识别英语作文
介绍人脸识别英语作文题目，The Application and Implications of Facial Recognition Technology。
In recent years, facial recognition technology has emerged as a groundbreaking innovation with profound implications across various sectors. This essay aims to explore the applications, benefits, and ethical concerns surrounding facial recognition technology.Facial recognition technology utilizes biometric data to identify individuals based on their unique facial features. It works by capturing and analyzing patterns, textures, and shapes of a person's face, often using algorithms to match them against a database of known faces. This technology has found widespread application in diverse fields, including security, law enforcement, marketing, and personal devices.One of the primary applications of facial recognitiontechnology is in the realm of security. Airports, border crossings, and high-security facilities use it to enhance surveillance and identify potential threats. By comparing faces against watchlists of known criminals or persons of interest, authorities can quickly identify and apprehend individuals. This capability has significantly bolstered security measures, helping prevent crime and safeguard public safety.Moreover, facial recognition technology has streamlined processes in various industries. In retail, it enables personalized marketing strategies by analyzing customer demographics and preferences. Retailers can use this data to tailor advertisements and promotions, improving customer engagement and driving sales. Additionally, facial recognition technology has simplified authentication procedures, replacing traditional methods like passwords or ID cards. Smartphones, laptops, and other electronic devices now utilize facial recognition for secure access, enhancing user convenience and security.In the realm of law enforcement, facial recognitiontechnology has proven instrumental in solving crimes and locating missing persons. Police departments utilize facial recognition systems to match surveillance footage with criminal databases, aiding in the identification and apprehension of suspects. This technology has helped expedite investigations and improve the efficiency of law enforcement agencies.However, despite its numerous benefits, facial recognition technology raises significant ethical concerns. One major issue is the potential for misuse and abuse of personal data. Facial recognition systems collect vast amounts of biometric information, raising concerns about privacy infringement and unauthorized surveillance. There are fears that governments or corporations could exploit this technology for mass surveillance, leading to a surveillance state where individuals' every move is monitored and recorded.Furthermore, facial recognition technology has been criticized for its inherent biases and inaccuracies, particularly concerning gender and race. Studies have shownthat these systems often exhibit higher error rates when identifying individuals with darker skin tones or non-binary genders. Such biases can result in wrongful arrestsor discrimination, exacerbating existing socialinequalities and injustices.In response to these concerns, there have been callsfor greater regulation and oversight of facial recognition technology. Some advocacy groups advocate for stricter privacy laws and transparency measures to protectindividuals' rights and hold companies accountable fortheir use of facial recognition systems. Additionally,there have been calls for the development of more accurate and unbiased algorithms through diverse and inclusive data sets.In conclusion, facial recognition technology represents a powerful tool with diverse applications and significant implications for society. While it offers numerous benefits in terms of security, convenience, and efficiency, it also raises profound ethical concerns regarding privacy, bias, and discrimination. As this technology continues to advance,it is essential to strike a balance between innovation and ethical considerations to ensure that facial recognition serves the greater good while respecting individuals' rights and dignity.。
人工智能人脸识别作文500字
人工智能人脸识别作文500字## English Answer:Artificial intelligence facial recognition is a rapidly developing field of computer science that uses machine learning algorithms to identify or verify a person's identity from a digital image or video. It has a wide range of applications, including security, law enforcement, and customer service.Face recognition systems work by extracting unique features from a person's face, such as the shape of their eyes, nose, and mouth. These features are then stored in a database and used to identify or verify a person's identity when they are presented with a new image or video.There are two main types of face recognition systems:2D face recognition and 3D face recognition. 2D face recognition systems use images of a person's face to identify or verify their identity. 3D face recognitionsystems use 3D models of a person's face to identify or verify their identity.2D face recognition systems are less accurate than 3D face recognition systems, but they are also less expensive. 3D face recognition systems are more accurate than 2D face recognition systems, but they are also more expensive.Face recognition technology has a number of advantages. It is fast, accurate, and non-invasive. It can also be used in a variety of applications. However, face recognition technology also has some disadvantages. It can be fooled by disguises, and it can be used to violate people's privacy.Overall, face recognition technology is a powerful tool that has the potential to improve our lives. However, it is important to be aware of the potential risks and benefits of face recognition technology before using it.## 中文回答：人工智能人脸识别。
Python3利用face_recognition实现人脸识别的方法
Python3利⽤face_recognition实现⼈脸识别的⽅法前⾔之前实践了下face++在线⼈脸识别版本，这回做⼀下离线版本。
github 上⾯有关于face_recognition的相关资料，本⼈只是做个搬运⼯，对其中的⼀些内容进⾏搬运，对其中⼀些例⼦进⾏实现。
官⽅描述：face_recognition是⼀个强⼤、简单、易上⼿的⼈脸识别开源项⽬，并且配备了完整的开发⽂档和应⽤案例，特别是兼容树莓派系统。
本项⽬是世界上最简洁的⼈脸识别库，你可以使⽤Python和命令⾏⼯具提取、识别、操作⼈脸。
本项⽬的⼈脸识别是基于业内领先的C++开源库 dlib中的深度学习模型，⽤Labeled Faces in the Wild⼈脸数据集进⾏测试，有⾼达99.38%的准确率。
但对⼩孩和亚洲⼈脸的识别准确率尚待提升。
（关于兼容树莓派，以后有板⼦了再做⼀下）下⾯两个链接划重点环境配置ubuntu16.04（其他环境的安装可以参考第⼀个链接，官⽅有说明）pycharm(可忽略,怎么舒服怎么来)python3opencv(我的是4.1.2，三点⼏的版本应该也⼀样）实际上只需要安装face_recognition，当然，没有opencv的也需要安装⼀下opencvpip3 install face_recognition图⽚准备由于需要做⼀些图⽚的⽐对，因此需要准备⼀些图⽚，本⽂图⽚取⾃以下链接接下来开始操作官⽅还有提供命令⾏的操作（这个没去做），本⽂不做这个，我们只要是要在python中⽤face_recognition，因此定位到这⼀块。
这个api⽂档地址就是上⾯的第⼆个链接。
进去之后可以看到：part1.识别图⽚中的⼈是谁代码# part1# 识别图⽚中的⼈是谁import face_recognitionknown_image = face_recognition.load_image_file("lyf1.jpg")unknown_image = face_recognition.load_image_file("lyf2.jpg")lyf_encoding = face_recognition.face_encodings(known_image)[0]unknown_encoding = face_recognition.face_encodings(unknown_image)[0]results = face_pare_faces([lyf_encoding], unknown_encoding)# A list of True/False values indicating which known_face_encodings match the face encoding to checkprint(type(results))print(results)if results[0] == True:print("yes")else:print("no")结果<class 'list'>[True]yespart2.从图⽚中找到⼈脸代码# part2# 从图⽚中找到⼈脸（定位⼈脸位置）import face_recognitionimport cv2image = face_recognition.load_image_file("lyf1.jpg")face_locations_useCNN = face_recognition.face_locations(image,model='cnn')# model – Which face detection model to use. “hog” is less accurate but faster on CPUs.# “cnn” is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is “hog”.face_locations_noCNN=face_recognition.face_locations(image)# A list of tuples of found face locations in css (top, right, bottom, left) order# 因为返回值的顺序是这样⼦的，因此在后⾯的for循环⾥⾯赋值要注意按这个顺序来print("face_location_useCNN:")print(face_locations_useCNN)face_num1=len(face_locations_useCNN)print(face_num1) # The number of facesprint("face_location_noCNN:")print(face_locations_noCNN)face_num2=len(face_locations_noCNN)print(face_num2) # The number of faces# 到这⾥为⽌，可以观察两种情况的坐标和⼈脸数，⼀般来说，坐标会不⼀样，但是检测出来的⼈脸数应该是⼀样的# 也就是说face_num1 ＝ face_num２； face_locations_useCNN 和 face_locations_noCNN 不⼀样org = cv2.imread("lyf1.jpg")img = cv2.imread("lyf1.jpg")cv2.imshow("lyf1.jpg",img) # 原始图⽚# Go to get the data and draw the rectangle# use CNNfor i in range(0,face_num1):top = face_locations_useCNN[i][0]right = face_locations_useCNN[i][1]bottom = face_locations_useCNN[i][2]left = face_locations_useCNN[i][3]start = (left, top)end = (right, bottom)color = (0,255,255)thickness = 2cv2.rectangle(img, start, end, color, thickness) # opencv ⾥⾯画矩形的函数# Show the resultcv2.imshow("useCNN",img)# for face_location in face_locations_noCNN:## # Print the location of each face in this image# top, right, bottom, left = face_location# # 等价于下⾯的这种写法for i in range(0,face_num2):top = face_locations_noCNN[i][0]right = face_locations_noCNN[i][1]bottom = face_locations_noCNN[i][2]left = face_locations_noCNN[i][3]start = (left, top)end = (right, bottom)color = (0,255,255)thickness = 2cv2.rectangle(org, start, end, color, thickness)cv2.imshow("no cnn ",org)cv2.waitKey(0)cv2.destroyAllWindows()结果face_location_useCNN:[(223, 470, 427, 266)]1face_location_noCNN:[(242, 489, 464, 266)]1图⽚效果⼤致是这样part3.找到⼈脸并将其裁剪打印出来（使⽤cnn定位⼈脸）代码# part3# 找到⼈脸并将其裁剪打印出来（使⽤cnn定位⼈脸）from PIL import Imageimport face_recognition# Load the jpg file into a numpy arrayimage = face_recognition.load_image_file("lyf1.jpg")face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn") print("I found {} face(s) in this photograph.".format(len(face_locations)))for face_location in face_locations:top, right, bottom, left = face_locationprint("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right)) face_image = image[top:bottom, left:right]pil_image = Image.fromarray(face_image)pil_image.show()结果I found 1 face(s) in this photograph.A face is located at pixel location Top: 205, Left: 276, Bottom: 440, Right: 512图⽚效果⼤致是这样part4.识别单张图⽚中⼈脸的关键点代码# part4 识别单张图⽚中⼈脸的关键点from PIL import Image, ImageDrawimport face_recognition# Load the jpg file into a numpy arrayimage = face_recognition.load_image_file("lyf1.jpg")# Find all facial features in all the faces in the imageface_landmarks_list = face_recognition.face_landmarks(image)# print(face_landmarks_list)print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))# Create a PIL imagedraw object so we can draw on the picturepil_image = Image.fromarray(image)d = ImageDraw.Draw(pil_image)for face_landmarks in face_landmarks_list:# Print the location of each facial feature in this imagefor facial_feature in face_landmarks.keys():print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))# Let's trace out each facial feature in the image with a line!for facial_feature in face_landmarks.keys():d.line(face_landmarks[facial_feature], width=5)# Show the picturepil_image.show()结果I found 1 face(s) in this photograph.The left_eyebrow in this face has the following points: [(305, 285), (321, 276), (340, 277), (360, 281), (377, 288)] The right_eye in this face has the following points: [(422, 313), (432, 303), (446, 302), (459, 305), (449, 312), (435, 314)]The nose_bridge in this face has the following points: [(394, 309), (394, 331), (395, 354), (396, 375)]The right_eyebrow in this face has the following points: [(407, 287), (424, 278), (442, 273), (461, 272), (478, 279)] The bottom_lip in this face has the following points: [(429, 409), (419, 421), (408, 428), (398, 430), (389, 429), (377, 424), (364, 412), (370, 413), (389, 414), (398, 415), (407, 413), (423, 411)]The chin in this face has the following points: [(289, 295), (291, 323), (296, 351), (303, 378), (315, 403), (332, 428), (353, 448), (376, 464), (400, 467), (422, 461), (441, 444), (459, 425), (473, 403), (484, 377), (490, 351), (493, 323), (493, 296)]The top_lip in this face has the following points: [(364, 412), (377, 407), (389, 403), (397, 406), (406, 402), (417, 405), (429, 409), (423, 411), (406, 412), (397, 414), (389, 413), (370, 413)]The left_eye in this face has the following points: [(327, 308), (339, 304), (353, 306), (364, 314), (352, 317), (338,316)]The nose_tip in this face has the following points: [(375, 383), (386, 387), (396, 390), (407, 385), (416, 381)]图⽚效果到此这篇关于Python3 利⽤face_recognition实现⼈脸识别的⽅法的⽂章就介绍到这了,更多相关Python3 ⼈脸识别内容请搜索以前的⽂章或继续浏览下⾯的相关⽂章希望⼤家以后多多⽀持！。
介绍人脸识别英语作文
介绍人脸识别英语作文## Facial Recognition ##。
English Answer：Facial recognition is a computer-vision technology used to identify or verify a person's identity using theirfacial characteristics. It is based on the idea that each person's face is unique and can be distinguished from others. The technology works by analyzing the facial features of an individual, such as the shape of their face, the distance between their eyes, the shape of their nose, and the curve of their lips, and comparing them to a database of known faces, or creating a new entry if the face is unrecognized. Facial recognition is a non-invasive and user-friendly technology that can be used in a wide range of applications.Facial recognition technology has several advantages. Firstly, it is highly accurate and reliable. Withadvancements in machine learning algorithms and image processing techniques, facial recognition systems can now achieve accuracy rates of over 99% under ideal conditions. Secondly, facial recognition is a non-contact and non-intrusive method, which makes it convenient and user-friendly. Users do not need to touch or interact with any devices, making it a hygienic and efficient solution for identity verification. Finally, facial recognition is a passive and covert technology, meaning that individuals do not need to be aware that their faces are being recognized. This makes it a powerful tool for surveillance and security applications.Despite its advantages, facial recognition technology also has several disadvantages. One major concern is privacy. Facial recognition systems create and store large databases of facial images, which raises concerns about the misuse of such data. Unauthorized access to these databases could lead to identity theft, stalking, or even discrimination. Another concern with facial recognition is bias. Facial recognition algorithms have been found to be less accurate for certain demographics, such as women andpeople of color, due to historical biases in the training data. This can lead to unfair and discriminatory outcomes when using facial recognition for decision-making purposes.中文回答：面部识别。
人脸识别的作文英文
人脸识别的作文英文英文回答：Facial recognition technology has rapidly evolved in recent years, presenting both opportunities and challenges. It offers the potential to enhance security, convenience, and accessibility, but it also raises concerns about privacy and discrimination.One of the primary advantages of facial recognition is its effectiveness in identifying individuals. Unlike traditional methods that rely on passwords or tokens,facial recognition uses unique biometric data that is difficult to forge or replicate. This makes it an ideal solution for high-security applications, such as border control, law enforcement, and financial transactions.Furthermore, facial recognition can greatly enhance convenience. It eliminates the need for memorizing and entering passwords, making it easier and faster to accessdevices, accounts, and facilities. For instance, smartphones and laptops can be unlocked with just a glance, and contactless payment systems can be initiated by simply looking at a camera.Moreover, facial recognition can promote accessibility for individuals with disabilities. For those who have difficulty typing or using traditional identification methods, facial recognition offers a secure and convenient alternative. It can enable them to participate more fully in digital and physical environments.However, alongside these benefits, facial recognition also raises concerns about privacy and discrimination. The collection and storage of facial data by governments, businesses, and individuals raise questions about the potential for misuse or surveillance. Additionally, there are concerns that facial recognition systems may be biased, leading to inaccurate or unfair identification results, particularly for certain demographics.To mitigate these concerns, it is essential toimplement robust privacy protections and ethical guidelines for the use of facial recognition technology. Governments and regulators should establish clear standards for data collection, storage, and usage, while individuals should be provided with informed consent and control over theirfacial data. Additionally, ongoing research and development should focus on improving the accuracy and fairness offacial recognition systems.In conclusion, facial recognition technology offers significant potential for enhancing security, convenience, and accessibility. However, it is crucial to address the privacy and discrimination concerns associated with its use. By implementing appropriate safeguards and ethical guidelines, we can harness the benefits of facialrecognition while protecting individual rights and ensuring equitable outcomes.中文回答：人脸识别技术近年来迅速发展，既带来了机遇，也带来了挑战。
人脸识别的便捷性与安全性的英语作文
人脸识别的便捷性与安全性的英语作文Facial recognition technology has become increasingly prevalent in our daily lives, offering both convenience and security. One of the most significant advantages of this technology is its ability to streamline processes. For instance, facial recognition enables quick identification, allowing users to unlock their devices or gain access to secure areas with just a glance. This ease of use is particularly beneficial in busy environments, such as airports and offices, where time-saving measures are essential.Moreover, facial recognition enhances security by providing a reliable means of identification. It is employed in various sectors, including law enforcement, banking, and public safety, to prevent unauthorized access and identify potential threats. With advanced algorithms and databases, this technology can accurately match faces, making it a powerful tool for surveillance and crime prevention.However, the implementation of facial recognition technology does raise concerns regarding privacy anddata security. There are fears that personal information could be misused or that individuals might be monitored without their consent. To address these issues, it is crucial to establish regulations that protect citizens' rights while allowing for the benefits of this technology to be realized.In conclusion, facial recognition technology offers remarkable convenience and enhances security in various applications. Nonetheless, it is essential to find a balance between leveraging its benefits and safeguarding individual privacy. With proper regulations and ethical considerations, facial recognition can be a valuable asset in our increasingly digital world.中文翻译：人脸识别技术在我们的日常生活中变得越来越普遍，提供了便利和安全的双重优势。
名词解释20
名词解释20浏览器（Browser）：也称为网页浏览器，是一种能够解读和显示互联网上网页的软件。
用户通过浏览器可以输入网址、搜索关键词、浏览网页内容、播放视频等操作。
搜索引擎（Search Engine）：是一种能够从互联网上获取并组织信息，提供搜索结果的程序。
用户通过输入关键词，搜索引擎能够找到与关键词相关的网页、图片、视频、文件等信息。
社交媒体（Social Media）：指通过互联网和移动设备等电子媒体平台，让用户建立和分享信息、观点、个人资料和其他内容的交流方式。
社交媒体包括微信、微博、Facebook、Twitter 等。
云计算（Cloud Computing）：是一种基于互联网的计算模式，将数据和计算资源存储在云端的服务器上，通过网络提供按需访问。
用户无需购买昂贵的硬件设备和软件，只需通过云计算平台使用所需的计算资源。
人工智能（Artificial Intelligence）：是一种模拟和实现人类智能的技术和应用，包括机器学习、自然语言处理、图像识别等。
人工智能可以用于自动驾驶、语音助手、智能翻译等领域。
区块链（Blockchain）：是一种分布式数据库技术，采用多个节点来存储数据，并通过密码学算法确保数据的安全性和可信度。
区块链常用于加密货币、供应链管理、智能合约等领域。
物联网（Internet of Things）：是将各种物理设备连接到互联网的网络。
通过物联网，用户可以通过手机或其他设备远程控制和监测家居、车辆、工厂等物体。
虚拟现实（Virtual Reality）：是一种计算机技术，通过戴上专用设备，用户可以体验到仿佛置身于另一个虚拟世界的感觉。
虚拟现实技术广泛应用于游戏、娱乐、教育等领域。
增强现实（Augmented Reality）：是一种技术，通过将计算产生的虚拟信息叠加到现实世界中，增强用户的感知和交互体验。
增强现实可以用于虚拟导航、教育学习、实时翻译等方面。
云存储（Cloud Storage）：是一种将数据存储在云服务器上的技术。
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Questions
1988
1991
Real time automated face recognition
Implemented in 2001 super bowl
FERET
1993-1997 The Face Recognition Technology Evaluation Sponsored by Defense Advances Research Products Agency
Fishers Faces
Maximize between-class variance Minimize within-class variance
EBGM
Relies on concept of nonlinear features
Lighting Pose
Expression
Given a still or video image of a scene, Identify or verify one or more persons in the scene using a stored database of faces
A Brief History
1960’s
Facial Recognition
By Lisa Tomko
Overview
What is facial recognition? History The Face Recognition Technology Evaluation
The Face Recognition Vender Test
Facial Recognition Grand Challenge Principle Components Analysis Linear Description Analysis Elastic Bunch Graph Matching
Βιβλιοθήκη Applications
Research
What is facial recognition?
Images must all be the same size and normalized Uses Data compression to reduce the detentions of the data and removes information that is not useful Decomposes facial structure into orthogonal components known as eigenfaces, stored in a 1D array
First semi-automated system Programs designed by Woody Bledsoe, Helen Chan Wolf and Charles Bisson. Linear algebra technique implemented Less than 100 values needed to align and normalize a face. Turk and Pentland
Entertainment
Areas of Research
MIT-multidimensional morphable models, view-based human face detection, cortex-like mechanisms, and object detection by components.
High Resolution photos 3D Face Scans Iris images
10x more effective then 2002 100x more effective than 1995
PCA
Pioneered by Kirby and Sirivich in 1988
Creates a dynamic link architecture that projects the face onto a grid Garbor jet is a node which describes image behaviors around pixels Garbor filter extracts shapes and detects features Accurate land mark localization is needed
Encouraged development of face recognition algorithms
FRVT
2000, 2002, and 2006
The Face Recognition Vender Test Evaluate work of FERET Assess commercial facial recognition Educate public
Pro: Only needs 1/1000 of data presented
Con: Needs full frontal face
LDA
Each face is represented by a large number of pixel values
Used to reduce number of features to a more manageable number before classification
In 2002- 90% verification and 1% false accept rates
FRGC
Facial Recognition Grand Challenge
Evaluated the latest in face recognition algorithms Used:
MSU-data clustering, statistical pattern recognition, face detection in color images, the use of faces and fingerprints for personal identification, and kernel principal component analysis. UCSD- use of shape contexts for object recognition, slow feature analysis, classifying facial actions, and face recognition using independent component analysis.
Applications
Law enforcement
Facial recognition using various databases SocialCamera SceneTap FaceR Celebrity TV set top box
Mobile applications