Face Machine 04 (表情库)

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人脸识别综述

人脸识别综述摘要:首先介绍了人脸识别的发展历程及基本分类；随后对人脸识别技术方法发展过程中一些经典的流行的方法进行了比较详细的阐述。

最后介绍了人脸识别的应用及发展现状，总结了人脸识别所面临的困难。

关键词:人脸识别1引言人脸是人类最重要的生物特征之一，反映了很多重要的生物信息，如身份，性别，种族，年龄，表情等等。

随着计算机技术的飞速发展，基于人脸图像的计算机视觉和模式识别问题也成为近些年研究的热点问题。

其中包括人脸检测，人脸识别，人脸表情识别等各类识别问题。

对于人脸识别问题的研究已有几十年的时间，在理论研究和实际开发方面都取得了一定的进展，并且目前已有一些电子产品配备了人脸识别系统。

但是，对于人脸性别和种族识别的研究却比较少，但研究这个问题的意义和实际价值却是不可忽视的。

在实际公共场所的安检系统中，大多数情况下都是将多种模式识别系统结合在一起，以尽量提高检测识别的准确度，性别识别系统也是其中不可缺少的一部分。

对它的研究不仅有助于提供更多个性化的人机交互方式，还可以应用于各种监控系统、电子产品的用户身份鉴别和信息采集系统。

从理论意义上来说，也丰富了原有的人脸识别方法，使得人脸识别系统不但可以识别出被识别者是谁，还能自动给出其性别和种族，从而提高人脸识别的准确率和图像检索效率。

所谓人脸识别,就是利用计算机分析人脸视频或者图像,并从中提取出有效的识别信息,最终判别人脸对象的身份。

人脸与人体的其他生物特征(指纹、虹膜等)一样与生俱来,它们所具有的唯一性和不易被复制的良好特性为身份鉴别提供了必要的前提；同其他生物特征识别技术相比,人脸识别技术具有操作简单、结果直观、隐蔽性好的优越性。

因此,人脸识别在信息安全、刑事侦破、出入口控制等领域具有广泛的应用前景。

2人脸识别的发展历程及方法分类关于人脸识别的研究最早始于心理学家们在20世纪50年代的工作，而真正从工程应用的角度来研究它则开始于20世纪60年代。

最早的研究者是Bledsoe,他建立了一个半自动的人脸识别系统,主要是以人脸特征点的间距、比率等参数为特征。

可爱的大脸娃娃符号库

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人脸表情识别英文参考资料

二、（国外）英文参考资料1、网上文献2、国际会议文章（英文）[C1]Afzal S, Sezgin T.M, Yujian Gao, Robinson P. Perception of emotional expressions in different representations using facial feature points. In: Affective Computing and Intelligent Interaction and Workshops, Amsterdam,Holland, 2009 Page(s): 1 - 6[C2]Yuwen Wu, Hong Liu, Hongbin Zha. Modeling facial expression space for recognition In:Intelligent Robots and Systems,Edmonton,Canada,2005: 1968 – 1973 [C3]Y u-Li Xue, Xia Mao, Fan Zhang. Beihang University Facial Expression Database and Multiple Facial Expression Recognition. In: Machine Learning and Cybernetics, Dalian,China,2006: 3282 – 3287[C4] Zhiguo Niu, Xuehong Qiu. Facial expression recognition based on weighted principal component analysis and support vector machines. In: Advanced Computer Theory and Engineering (ICACTE), Chendu,China,2010: V3-174 - V3-178[C5] Colmenarez A, Frey B, Huang T.S. A probabilistic framework for embedded face and facial expression recognition. 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Facial expression recognition using Stochastic Neighbor Embedding and SVMs. In: System Science and Engineering (ICSSE), Macao, China, 2011: 671 - 674[C16] Junhua Li, Li Peng. Feature difference matrix and QNNs for facial expression recognition. In: Control and Decision Conference, Yantai, China, 2008: 3445 - 3449 [C17] Yuxiao Hu, Zhihong Zeng, Lijun Yin,Xiaozhou Wei, Jilin Tu, Huang, T.S. A study of non-frontal-view facial expressions recognition. In: Pattern Recognition, Tampa, FL, USA,2008: 1 - 4[C18] Balasubramani A, Kalaivanan K, Karpagalakshmi R.C, Monikandan R. Automatic facial expression recognition system. In: Computing, Communication and Networking, St. Thomas,USA, 2008: 1 - 5[C19] Hui Zhao, Zhiliang Wang, Jihui Men. Facial Complex Expression Recognition Based on Fuzzy Kernel Clustering and Support Vector Machines. In: Natural Computation, Haikou,Hainan,China,2007: 562 - 566[C20] Khanam A, Shafiq M.Z, Akram M.U. Fuzzy Based Facial Expression Recognition. 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Extension of cascaded simple feature based face detection to facial expression recognition. Pattern Recognition Letters, 2008, 29(11): 1621-1631[J48] Y. Zhu, L.C. De Silva, C.C. Ko. Using moment invariants and HMM in facial expression recognition. Pattern Recognition Letters, 2002, 23(1-3): 83-91[J49] Jun Wang, Lijun Yin. Static topographic modeling for facial expression recognition and analysis. Computer Vision and Image Understanding, 2007, 108(1-2): 19-34[J50] Caifeng Shan, Shaogang Gong, Peter W. McOwan. Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing, 2009, 27(6): 803-816[J51] Xue-wen Chen, Thomas Huang. Facial expression recognition: A clustering-based approach. Pattern Recognition Letters, 2003, 24(9-10): 1295-1302 [J52] Irene Kotsia, Ioan Buciu, Ioannis Pitas. An analysis of facial expression recognition under partial facial image occlusion. 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MATLAB中的人脸识别与表情分析技巧

MATLAB中的人脸识别与表情分析技巧人脸识别和表情分析作为计算机视觉领域中的重要研究方向，在实际应用中有着广泛的应用。

作为计算机视觉领域的一个重要工具，MATLAB提供了丰富的功能和库，便于开发人员进行人脸识别与表情分析任务。

本文将介绍MATLAB中实现人脸识别与表情分析的一些技巧和方法。

一、人脸识别的基本原理与实现人脸识别是指通过计算机自动识别图像或视频中的人脸，核实或识别其中的个体身份。

其核心任务包括人脸检测、人脸对齐、特征提取和识别等过程。

在实际应用中，人脸识别常用于犯罪侦查、人脸门禁、人脸支付等领域。

在MATLAB中，实现人脸识别可以借助于OpenCV库。

首先，我们需要使用OpenCV的人脸检测算法来获取图像或视频中的人脸位置。

接着，通过对检测到的人脸进行对齐和预处理，将其转换为统一大小的灰度图像。

然后，利用人脸图像的特征提取算法，如主成分分析(PCA)、线性判别分析(LDA)等，将人脸图像转换为固定长度的特征向量。

最后，通过比对输入的人脸特征向量与保存的人脸数据库中的特征向量，即可进行人脸识别。

除了OpenCV库外，MATLAB还提供了自身的人脸识别库，如Computer Vision Toolbox中的vision.CascadeObjectDetector和vision.FaceRecognizer等函数，可以简化人脸检测和识别的过程。

使用这些函数，我们只需加载预训练好的人脸检测和识别模型，然后输入图像或视频，即可实现人脸识别的功能。

二、表情分析的基本原理与实现表情分析是指分析人脸图像或视频中的表情信息，识别出人脸所表现出的情绪状态，如喜、怒、哀、乐等。

表情分析在情感计算、人机交互和心理学研究等领域有着广泛的应用。

在MATLAB中，实现表情分析可以通过机器学习的方法。

首先，我们需要获取图片或视频中的人脸位置，可以借助OpenCV库或Computer Vision Toolbox提供的函数。

人脸数据集地址

USTC-NVIE Database[(natural visible and infrared facial expression database)](由中国科学技术大学安徽省计算与通信软件重点实验室建成并发布，是目前世界较为全面的人脸表情数据库，其中包含大约100名被试三种光照条件下六种表情的可见图像以及长波红外图像，另外表情又分为自发表情与人为表情，人为表情又分为戴眼镜与不戴眼镜两种情况。

为进行（自发+人为）表情识别与情绪分析推理实验提供了充足的实验样本与数据)数据库主页：/发布地址：http://sspnet.eu/2010/08/ustc-nvie-natural-visible-and-infrared-facial-expression-database/■Annotated Database (Hand, Meat, LV Cardiac, IMM face) (Link)■AR Face Database (Link)■BioID Face Database (Link)■Caltech Computational Vision Group Archive (Cars, Motorcycles, Airplanes, Faces, Leaves, Background) (Link)■Carnegie Mellon Image Database (motion, stereo, face, car, ...) (Link)■CAS-PEAL Face Database (Link)■CMU Cohn-Kanade AU-Coded Facial Expression Database (Link)■CMU Face Detection Databases (Link)■CMU Face Expression Database (Link)■CMU Face Pose, Illumination, and Expression (PIE) Database (Link)■CMU VAS C Image Database (motion, road sequences, stereo, CIL’s stereo data with ground truth, JISCT, face, face expressions, car) (Link)■Content-based Image Retrieval Database (Link)■Face Video Database of the Max Planck Institute for Biological Cybernetics (Link)■FERET Database (Link)■FERET Color Database (Link)■Georgia Tech Face Database (Link)■German Fingerspelling Database (Link)■Indian Face Database ([url=/~vidit/IndianFaceDatabase/]Link[/url])■MIT-CBCL Car Database (Link)■MIT-CBCL Face Recognition Database (Link)■MIT-CBCL Face Databases (Link)■MIT-CBCL Pedestrian Database (Link)■MIT-CBCL Street Scenes Database (Link)■NIST/Equinox Visible and Infrared Face Image Database (Link)■NIST Fingerprint Data at Columbia (Link)■ORL Database of Faces (Link)■Rutgers Skin Texture Database (Link)■The Japanese Female Facial Expression (JAFFE) Database (Link)■The Ohio State University SAMPL Image Database (3D, still, motion) (Link)■The University of Oulu Physics-Based Face Database (Link)■UMIST Face Database (Link)■USF Range Image Data (with ground truth) (Link)■Usenix Face Database (hundreds of images, several formats) (Link)■UCI Machine Learning Repository (Link)■USC-SIPI Image Database (collection of digitized images) (Link)■UCD VALID Database (multimodal for still face, audio, and video) (Link)■UCD Color Face Image (UCFI) Database for Face Detection (Link)■UCL M2VTS Multimodal Face Database (Link)■Vision Image Archive at UMass (sequences, stereo, medical, indoor, outlook, road, underwater, aerial, satellite, space and more) (Link)■Where can I find Lenna and other images? (Link)■Yale Face Database (Link)■Yale Face Database B (Link)。

[2015](face++)Naive-Deep Face Recognition Touching the Limit of LFW Benchmark or Not

DeepID2+
DeepFace
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Figure 1. A data perspective to the LFW history. Large amounts of web-collected data is coming up with the recent deep learning waves. Extreme performance improvement is gained then. How does big data impact face recognition?
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DeepID2 GaussianFace DeepID TL Joint Bayesian High-dim LBP Tom-vs-Pete Bayesian Face Revisited Associate-Predict Hybrid Deep Learning FR+FCN
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
Erjin Zhou Face++, Megvii Inc.
zej@
Zhimin Cao Face++, Megvii Inc.
czm@
and requires very low false positive rate. Unfortunately, empirical results show that a generic method trained with webcollected data and high LFW performance doesn’t imply an acceptable result on such an application-driven benchmark. When we keep the false positive rate in 10−5 , the true positive rateis 66%, which does not meet our application’s requirement. By summarizing these experiments, we report three main challenges in face recognition: data bias, very low false positive criteria, and cross factors. Despite we achieve very high accuracy on the LFW benchmark, these problems still exist and will be ampliﬁed in many speciﬁc real-world applications. Hence, from an industrial perspective, we discuss several ways to direct the future research. Our central concern is around data: how to collect data and how to use data. We hope these discussions will contribute to further study in face recognition.

人脸表情估计与表情合成完整emoji表情包大全

人脸表情估计与表情合成完整emoji表情包大全人脸表情估计与表情合成完整emoji表情包大全如果把人们之间交流时传递的进行分割的话，人们所说的话语传递的信息占了7%，说话时的语调占了38%，而说话人的表情占到了55%。

从表情和神色的变化中，可以感知到一个人的情绪、感受、甚至秉性和气质。

人脸表情是人类进行相互交流的基础，通过人脸表情所能传达的信息大大超过通过语音或动作所能传达的信息。

如果把人们之间交流时传递的信息进行分割的话，人们所说的话语传递的信息占了7%，说话时的语调占了38%，而说话人的表情占到了55%。

人的脸上分布着五十多块面部肌肉，这些肌肉的不同运动方式会导致不同脸部表情，从这些表情和神色的变化中，可以感知到一个人的情绪、感受，甚至秉性和气质。

一般来说，表情可以分为中性无表情和六种基本表情: 高兴、忧伤、惊讶、愤怒、鄙视和恐惧，其他表情可以看做是这些表情的组合。

表情估计人脸表情是影响人脸识别系统性能的一个重要因素，一般的人脸识别系统数据库中存储的是中性无表情的人脸图像，如果以带有各种表情的人脸图像去进行识别查询就往往得不到所期望的结果。

解决这个问题的可能方法有: 在库中保存每一个人的所有可能的表情，但这对于变化无穷的人脸表情来说是不实际的; 采用对表情不敏感的识别算法，但这样往往只能对低强度的表情变化有效，而且对表情的不敏感很可能就会导致对其他影响因素敏感; 对输入图像进行表情补偿，这是解决表情对人脸识别系统的影响最有效的方法，可采用如图1所示的结构。

图1 人脸识别流程对于输入的人脸图像，首先采用表情估计模块估计图像中人的表情，如果此人是不带有表情的就直接可以交给人脸识别系统进行识别了。

而如果此人是带有表情的，则再输入到表情合成模块，表情合成模块会根据此人的表情导入相应的模型对其进行处理，然后合成得出此人的不带表情人脸图像，再交给人脸识别系统进行识别。

在人脸表情估计方面，采用的是人脸图像的Gabor特征，通过一系列的滤波器对图像进行处理，可以得出图中人脸在不同方向上不同大小的特征，由于Gabor函数具有与人类大脑皮层简单细胞的二维反射区相同的特性，因此用Gabor特征来体现人脸不同表情的不同变化是非常有效的。

面部表情识别fer流程

面部表情识别fer流程English Answer:1. Face Detection.The first step in FER is face detection, which involves identifying and localizing faces in an image or video frame. This is typically done using pre-trained deep learning models, such as the Haar Cascade Classifier or Faster R-CNN.2. Feature Extraction.Once faces have been detected, they are typically cropped and resized to a standard size. The next step is to extract features from these cropped faces. This involves identifying key characteristics of the face, such as the position of the eyes, nose, and mouth, as well as the shape and texture of the skin.3. Feature Selection.The extracted features are typically high-dimensional, so it is necessary to select a subset of features that are most relevant for FER. This can be done using techniques such as principal component analysis (PCA) or linear discriminant analysis (LDA).4. Classification.The selected features are then used to classify the face into a specific expression category. This is typically done using a machine learning algorithm, such as a support vector machine (SVM) or a deep neural network (DNN).Chinese Answer:1. 面部检测。

特殊字符大全

特殊字符大全汉字大全(1 2 3 4) 按部首查询字符大全! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ •￠￡¤ ￥| § ¨ a - ˉ ° ± 2 3 ′ μ · 1 o à á è é ê ì í D ò ó × ù ú ü Y T à á a è é ê ì í e ò ó ÷ ù ú ü y t ā ā ē ē ě ě ī ī ń ň ō ō ū ū ∥ ǎ ǎ ǐ ǐ ǒ ǒ ǔ ǔ ǖ ǖ ǘ ǘ ǚ ǚ ǜ ǜ ɑ ɡ ˇ ˉ ˊ ˋ ˙ Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω α β γ δ ε ζ η θ ι κ λ μ ν ξ ο π ρ σ τ υ φ χ ψ ω Ё А Б В Г Д Е Ж З И Й К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Ъ Ы Ь Э Ю Я а б в г д е ж з и й к л м н о п р с т у ф х ц ч ш щ ъ ы ь э ю я ё‐ –—― ‖‘ ’ “ ” ‥ … ‰ ′ ″ ‵ ※ ￣€ ℃ ℅ ℉ № ℡ Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ Ⅺ Ⅻ ⅰ ⅱ ⅲ ⅳ ⅴ ⅵ ⅶ ⅷ ⅸ ⅹ ← ↑ → ↓ ↖ ↗ ↘ ↙ ∈ ∏ ∑ ∕ ° √ ∝ ∞ ∟ ∠ ∣ ∥ ∧ ∨ ∩ ∪ ∫ ∮ ∴ ∵ ∶ ∷ ～∽ ≈ ≌ ≒ ≠ ≡ ≤ ≥ ≦ ≧ ≮ ≯ ⊕ ⊙ ⊥ ⊿ ⌒ ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ⑴ ⑵ ⑶ ⑷ ⑸ ⑹ ⑺ ⑻ ⑼ ⑽ ⑾ ⑿ ⒀ ⒁ ⒂ ⒃ ⒄ ⒅ ⒆ ⒇ ⒈ ⒉ ⒊ ⒋ ⒌ ⒍ ⒎ ⒏ ⒐ ⒑⒒ ⒓ ⒔ ⒕ ⒖ ⒗ ⒘ ⒙ ⒚ ⒛ ─ ━ │ ┃ ┄ ┅ ┆ ┇ ┈ ┉ ┊ ┋ ┌ ┍ ┎ ┏ ┐ ┑ ┒ ┓ └ ┕ ┖ ┗ ┘ ┙ ┚ ┛ ├ ┝ ┞ ┟ ┠ ┡ ┢ ┣ ┤ ┥ ┦ ┧ ┨ ┩ ┪ ┫ ┬ ┭ ┮ ┯ ┰ ┱ ┲ ┳ ┴ ┵ ┶ ┷ ┸ ┹ ┺ ┻ ┼ ┽ ┾ ┿ ╀ ╁ ╂ ╃ ╄ ╅ ╆ ╇ ╈ ╉ ╊ ╋ ═ ║ ╒ ╓ ╔ ╕ ╖ ╗ ╘ ╙ ╚ ╛ ╜ ╝ ╞ ╟ ╠ ╡ ╢ ╣ ╤ ╥ ╦ ╧ ╨ ╩ ╪ ╫ ╬ ╭ ╮ ╯ ╰ ╱ ╲ ╳ ▁ ▂ ▃ ▄ ▅ ▆▇ █ ▉ ▊ ▋ ▌ ▍ ▎ ▏ ▓ ▔ ▕ ■ □ ▲ △ ▼ ▽ ◆ ◇ ○ ◎ ● ◢ ◣ ◤ ◥ ★ ☆ ☉ ♀♂、。

Face Machine 02 (权重)

Face Machine使用方法面部表情的权重：上一章我们介绍了Face Machine 的关联绑定，但是我们会发现绑定之后会存在很多的问题，因为我们使用权重曲线的方法来分配绑定后的权重问题，所以在绑定后个别的部位的权重需要进一步细致的分配。

如图：（嘴部的变形错误）（眼睛的变形错误）我们选则模型运行运行命令绘制Face Machine权重。

我们会发现模型变成黑白两色并且鼠标变成笔刷：到这里我想有经验的动画人都知道下面该怎么做了，这个命令和我们Paint 的一系列权重笔刷没有区别，他的面版和骨骼权重面板一样。

选中控制嘴部的权重名字。

（被选中的名字为）先用Smooth笔刷将上下唇分开然后用其余的笔刷进行调节这里不多做介绍了。

最后的样子如图：（嘴上部的权重分配）嘴上部有两部分权重控制分为左右两部分我们只需要刷一面的权重即可—（当然看不习惯也可以两面都刷），之后我们用镜像权重来进行处理。

选择运行出现了属性框如图：Mirror across为镜像对称轴，Direction为镜像的方向，不勾选为负方向到正方向镜像，勾选反之。

（注意：我们在使用镜像权重的时候，要将其控制器的属性变为原始状态的0，运行命令后在调节观察，不然会很容易出现错误状况，再则就是注意另存，因为镜像无法返回，最后镜像每次使用会渐渐的镜像，达到完全镜像需要多运行几次命令。

）那么下面是眼皮的权重。

如图：眼皮的权重的名字被放在面板的最下面用来与眉弓、眼括等区分，所以眼皮的名字到后面找。

同样我们将眼皮部分刷白，来获得我们想要的效果。

如图：（上眼皮）（下眼皮）调节完左边右边镜像就完成了。

嘴部和眼睛调节完毕之后呢，我们要对脖子的权重进行整理，因为Face Machine 插件自动生成的权重分配比较僵硬，而最明显的地方位于脖子。

（当然如果你对位特别准的话这里也可能不需要调整.)如图：（脖子的错误变形）我们选择Paint 面板中的第一个名字对他进行权重的修改，将他的影响范围变大（就是将脖子到下巴的地方刷白）（刷完后的样子）这样明显的变形错误就解决了，如果你的模型上还有这样的明显变形错误，那么就说明你在前面的控制器对位上还有不足。

侧面人脸图像的识别方法(2)

得 Bk 是正交的，故有：
T
Bk Bk =I
（7）
再设如下式的方程：
T
X=Lk Bk 圯XBk =Lk
（8）
由式（4）和式（8）可得：
LY=Lk Bk
（9）
再将式（6）带入式（9）可得：
LY=Lk
Bk
=Lk（Λ-k1
T
准k
T
Y
Y）圯L=Lk（Λ-k1
T
准k
YT）
（10）
再将式（8）带入式（10）可得：
其中 ε=X-LY 是误差矢量，L 是 n×m 的矩阵，当误差 ε ε 很小时，上式便退化为线性模型，再通过这 s 对实验数据便可求解出 L。
3 正面图像的合成算法
合成算法分为几个步骤，首先对采集样本形成规范化训练集合，再通过主元分析降维并去除样本间的相关性，最后通过多变量线性退化模型求解出方程的系数，完成从侧面图像到正视图的合成，具体如下：
T
T -1 T T
XBk =Lk 圯L=XBk（Λk 准k Y ）
（11）
通过上面的过程，便可解得 X 和 Y 的线性系数矩阵 L。将
一幅侧面图像预处理后带入式（4）便可得到合成后的正面像。
4 实验与分析
实验数据来自 CVL FACE 数据库[7]，该人脸库以西方青年男性为主，每个人 7 张照片，这里建立了 90 个人的样本集，为方便处理，这里的侧面人脸图像均采用右向旋转 90°后的侧面像，图 2 为部分实验结果。
素颜色由图像 B 提供。如图 6 所示，1、2、3、5、6 由图像 A 提供，
T
X=[x1，x2，xi，…，xs]，其中 xi=[xi1，xi2，…，xin]
T

一种仿人机器人面部的结构设计

收稿日期: 2002 07 09 作者简介: 于爽( 1980- ) , 女( 汉) , 吉林, 硕士研究生
美国麻省理工学院的 C. Breazeal 博士等人提出的系统采用了马达驱动的脸部结构, 能够实现机器人与人类之间类似婴儿与护理者之间的相互交流[ 4] 。瑞士苏黎士大学的 H. Kobayashi 博士等人研制了由两个摄像头和一个嘴巴组成的简单结构的仿生面部系统, 并在其左眼球内安装了 CCD 摄像机已获得人类的表情数据。该机器人面部能够实现最典型表情的识别和表达[ 5] 。
人体头面部尺寸数据的分析
和统计计算构成了标准的主
要内容。将这个标准的平均尺寸同时放大 1. 2 倍作为参考尺寸, 当然, 这个设计也不
图 1 自由度分布图
能完全的拘泥于这个尺寸, 同时从美学的角度出发进行了适当的调整, 从而设计出五官匀称的机器人的面部。
合金, 它具有较高强度和腐蚀稳定性, 在退火状态下塑性良好, 焊缝的塑性良好, 气焊和电焊的焊接头强度为基本强度
的 90 % ~ 95 % , 切削性良好 , 用于制作受力零件。 ( 4) 移动导杆。为减少与滑块的摩擦力, 选用铜。
( 5) 眼球。采用树脂。 2. 3 具体结构设计
在设计中传动环节均采用了梯形齿同步带, 它具有速比准确, 传动效率高等特点。驱动元件均选用两相步进电
机, 它可直接实现数字控制, 控制结构简单, 控制性能好, 而且成本低廉。
机器人脸在 0. 2 s 内完成每一种表情的运动过程, 电机转速和各器官的运动范围数据如表 2 所示。
机构
最大运动范围运动完成时间( s) 电机转速( r/ min)

词达人词汇大赛题库

词达人词汇大赛题库一、单选题（每题5分，共100分）1. The two countries are going to meet to ______ some barriers to trade between them.A. make up.B. use up.C. turn down.D. break down.答案：D。

解析：make up意为“组成；编造；弥补”；use up意为“用完，耗尽”；turn down意为“拒绝；调小（音量等）”；break down有“分解；（机器等）出故障；（谈判等）失败；消除（障碍等）”的意思，在这里表示消除两国之间贸易的一些障碍，故选D。

2. I'm so ______ to all those volunteers because they helped myterrible day end happily.A. special.B. superior.C. grateful.D. attractive.答案：C。

解析：special意为“特殊的”；superior意为“上级的；优秀的”；grateful 意为“感激的”；attractive意为“吸引人的”。

根据“他们让我糟糕的一天愉快地结束”可知，我对志愿者们是感激的，所以选C。

3. -What do you think of the movie?-It's fantastic. The only pity is that I ______ the beginning of it.A. missed.B. had missed.C. miss.D. would miss.答案：A。

解析：根据语境，这部电影很棒，唯一的遗憾是我错过了开头部分。

这里是在陈述过去发生的一个事实，没有强调“错过”这个动作发生在另一个过去动作之前，所以用一般过去时，选A。

4. It is reported that a space station ______ on the moon in years to come.A. will be building.B. will be built.C. has been building.D. has been built.答案：B。

Cut-off and face machine

专利名称：Cut-off and face machine 发明人：Calvin C. Williamson申请号：US06/360905申请日：19820323公开号：US04430913A公开日：19840214专利内容由知识产权出版社提供摘要：A pipe facing machine performs cutting, bevelling and deburring operations on an end of a pipe that is stationarily held in the same position during all three operations.A tool head that rotates about the pipe carries knives for performing the various operations. Initially, two diametrically opposed cutting knives are advanced in a radially inward direction to engage the pipe while the tool head is rotating to cut it at a predetermined location. Thereafter, as the cutting knives are being withdrawn, a bevelling knife and a deburring knife are advanced radially inward to a point where the bevelling knife provides a desired bevel on the cut edge of the pipe. During the radially inward motion of the deburring knife, it is maintained at an axially displaced position where it does not engage the pipe. Once it is located radially within the interior of the pipe, it is axially moved to a position where it is in alignment with the cut, bevelled edge of the pipe, and it is then withdrawn in a radially outward direction simultaneously with the bevelling knife, to remove the burr provided on the interior edge of the pipe by the bevelling knife.申请人：KAISER STEEL CORPORATION代理人：James E. Toomey,James A. LaBarre更多信息请下载全文后查看。

SmackFace1000 模块说明书

SmackFace1000 模块用户手册目录1 简介 (5)1.1 SmackFace1000的目标 (5)1.2 SmackFace1000技术特征 (5)1.3 SmackFace1000为面部识别引擎的技术特性 (5)2 SmackFace1000的基本概念 (7)2.1 SmackFace1000 OEM模块的要求 (7)2.1.1 系统要求 (7)2.1.2 关于面部图像的建议 (7)2.2 用户分类（用户模式） (8)2.3 用户登记 (8)3 SmackFace1000的外部结构 (10)3.1 外部结构 (10)3.2 连接计算机 (11)3.3 标准操作 (11)4 如何使用SmackFace1000 OCX (13)4.1 属性 (13)4.1.1 SFMachineCount (13)4.1.2 SFVerifyLevel (13)4.1.3 WorkingOrgMode (13)4.1.4 SFDatabaseDir¹ (14)4.1.5 SFEnrollCount¹ (14)4.1.6 SFManEnrollState¹ (14)4.2 方法 (14)4.2.1 ConnectAll (14)4.2.2 DisconnectAll (15)4.2.3 SearchAvailableMachine (15)4.2.4 ConnectMachine (15)4.2.5 DisconnectMachine (15)4.2.6 GetCommMode (15)4.2.7 GetMachineIdx (16)4.2.8 GetMachineNo (16)4.2.9 SetMachineNo (16)4.2.10 GetIPAddr (16)4.2.11 SetIPAddr (17)4.2.12 GetCaptureMode (17)4.2.13 SetCaptureMode (17)4.2.14 GetBrightness (17)4.2.15 SetBrightness (18)4.2.16 CaptureImage (18)4.2.17 GetImageData (18)4.2.18 SaveImage (18)4.2.19 Display (19)4.2.20 IsFaceImage (19)4.2.21 IsFaceImageFile (19)4.2.22 ExtractFeatureFromDev (20)4.2.23 ExtractFeatureFromFile (20)4.2.24 Match (20)4.2.25 SendWiegand (21)4.2.26 CardReaderOn (21)4.2.27 BuzzerOn (21)4.2.28 LEDCardGreenOn (21)4.2.29 LEDCardRedOn (22)4.2.30 LEDFaceGreenOn (22)4.2.31 LEDFaceRedOn (22)4.2.32 SFAction (23)4.2.33 ManEnrollStart¹ (23)4.2.34 ManEnrollStop¹ (23)4.2.35 ManCapture¹ (23)4.2.36 Enroll¹ (24)4.2.37 OffLineEnroll¹ (24)4.2.38 RegisterItem¹ (25)4.2.39 Delete¹ (25)4.2.40 DeleteAll¹ (25)4.2.41 Verify¹ (25)4.2.42 VerifyFromFile¹ (26)4.2.43 SearchEmptyID¹ (26)4.2.44 GetIDFromCardno¹ (26)4.2.45 GetCardnoFromID¹ (26)4.2.46 GetUserName¹ (27)4.2.47 SetUserName¹ (27)4.2.48 GetUserType¹ (27)4.2.49 GetFeatureFromDB¹ (27)4.2.50 SetFeatureToDB¹ (28)4.2.51 GetLogCount¹ (28)4.2.52 GetLogInfo¹ (28)4.2.53 DeleteAllLog¹ (29)4.3 事件 (29)4.3.1 OnReceiveCardSign (29)4.3.2 OnVerify¹ (29)5 SmackFace1000软件包 (31)5.1 包的组成 (31)5.2 演示程序1(Visual Basic) (32)5.2.1 界面 (32)5.2.2 控制功能 (33)5.2.3 使用 (34)5.3 演示程序2(Visual C++) (36)5.3.1 界面 (36)5.3.2 功能和控制的使用 (37)1简介本手册描述了Smack Face1000，面部识别+ ID卡考勤机和门禁的设计特性。

基于Apex帧光流和卷积自编码器的微表情识别

2021574微表情[1]是一种自发的面部表情，持续时间短（通常在0.04~0.20s之间[2]），局部变化且变化强度低[3]，极少人能用裸眼观察到微表情。

微表情通常发生在人试图隐藏自己的真实情绪时，无法伪造也不能抑制[4]。

微表情能够反映人的真实情感，在刑侦审判、教学评估、婚姻关系预测、国家安全等领域都有潜在应用。

基于Apex帧光流和卷积自编码器的微表情识别温杰彬1，杨文忠1，2，马国祥3，张志豪1，李海磊11.新疆大学信息科学与工程学院，乌鲁木齐8300462.中国电子科学研究院社会安全风险感知与防控大数据应用国家工程实验室，北京1000413.新疆大学软件学院，乌鲁木齐830091摘要：针对跨库微表情识别问题，提出了一种基于Apex帧光流和卷积自编码器的微表情识别方法。

该方法包括预处理、特征提取、微表情分类三部分。

预处理部分对微表情进行Apex帧定位以及人脸检测和对齐；特征提取部分首先计算预处理过的Apex帧的TVL1光流，然后使用得到的水平和竖直光流分量图像训练卷积自编码器得到最优结构和参数；最后将两个分量自编码器中间层的特征融合后作为微表情的特征；微表情分类就是使用支持向量机（Support Vector Machine，SVM）对上一步中提取到的特征进行分类。

实验结果较基准方法（LBP-TOP）有了很大的提高，UF1提高了0.1344，UAR提高了0.1406。

该方法为微表情特征提取和识别提供了新的思路。

关键词：微表情识别；Apex帧；光流；卷积自编码器；支持向量机（SVM）文献标志码：A中图分类号：TP391doi：10.3778/j.issn.1002-8331.1911-0399Micro-expression Recognition Based on Apex Frame Optical Flow and Convolutional Autoencoder WEN Jiebin1,YANG Wenzhong1，2,MA Guoxiang3,ZHANG Zhihao1,LI Hailei11.College of Information Science and Engineering,Xinjiang University,Urumqi830046,China2.National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data（PSRPC）,China Academyof Electronics and Information Technology,Beijing100041,China3.School of Software,Xinjiang University,Urumqi830091,ChinaAbstract：Aiming at the problem of cross-database micro-expression recognition,a micro-expression recognition method based on Apex frame optical flow and convolutional autoencoder is proposed.The method includes three parts：prepro-cessing,feature extraction and micro-expression classification.The preprocessing section performs Apex frame positioning, face detection and alignment on the micro-expressions.The feature extraction section first calculates the TVL1optical flow of the pre-processed Apex frame,then uses the obtained horizontal and vertical optical flow component images to train the convolutional autoencoder to obtain the optimal structure and parameters,finally combines the two components from the features of the middle layer of the encoder as the features of the micro-expressions.In section of micro-expression classification,a Support Vector Machine（SVM）classifier is used to classify the features extracted in the previous step. The experimental results have been greatly improved compared to the baseline method（LBP-TOP）.Among them,UF1 has increased by0.1344,and UAR has increased by0.1406.This method provides new ideas for micro-expression fea-tures extraction and recognition.Key words：micro-expression recognition;Apex frame;optical flow;convolutional autoencoder;Support V ector Machine（SVM）基金项目：国家自然科学基金（U1603115）；社会安全风险感知与防控大数据应用国家工程实验室主任基金项目；四川省科技计划项目（WA2018-YY007）。

基于hugging face的代码

基于hugging face的代码[基于hugging face的代码]，以中括号内的内容为主题，写一篇1500-2000字文章，一步一步回答一、简介在本文中，我们将探讨基于hugging face的代码库——Transformer，介绍它的背景和功能，并一步一步回答中括号内的问题。

Transformer是一个自然语言处理领域的重要工具，可用于文本分类、情感分析、机器翻译等任务。

本文将通过实例代码解释如何使用Transformer来进行文本分类任务。

二、Transformer的背景Transformer是由Google于2017年提出的一种用于自然语言处理的模型架构。

它的创新之处在于引入了自注意力机制，并完全摒弃了传统的循环神经网络（RNN）和卷积神经网络（CNN）结构。

Transformer的成功开拓了处理自然语言的新思路，并为后续的研究工作提供了重要的基础。

三、Transformer的功能1. 文本分类（Text Classification）：将输入的文本分为不同的类别。

2. 情感分析（Sentiment Analysis）：判断文本的情感倾向，如正面、负面或中性。

3. 机器翻译（Machine Translation）：将一种语言的文本翻译为另一种语言的文本。

4. 命名实体识别（Named Entity Recognition）：识别文本中的命名实体，如人名、地名、组织机构名等。

四、案例：使用Transformer进行文本分类任务在这个示例中，我们将使用Transformer来进行一个基本的文本分类任务。

假设我们有一个数据集，其中包含许多电影评论和对应的情感标签（正面或负面）。

我们的目标是训练一个模型，它能够自动地对新的电影评论进行情感分类。

1. 数据预处理首先，我们需要对数据进行预处理。

将文本转换为模型可以理解的数值表示形式。

同时，还需将情感标签转换为0（负面）和1（正面）的二元表示形式。

the face machine 中文使用手册

The Face Machine 中文使用手册The Face Machine 中文使用手册激活插件首先安装插件，然后我们只需在maya里点击"Window > Settings / Preferences > Plug-in Manager，找到 faceMachine.ml 然后勾选load 即可，激活此插件。

装配过程概述：最开始，我们要做的就是给自己的模型添加一个面部匹配系统。

它由两个部分组成，分别是“匹配曲线”和“定位器”（如下图）。

匹配曲线将最终匹配你的模型，而定位器是用来控制匹配曲线的，它能调节匹配曲线的具体位置使其更好的匹配你的模型。

（当然这些只是前期的匹配系统，它们并不十分重要，因为它们将会被最终的动画控制器取代。

）因为这些匹配曲线将直接影响模型的权重，所以当面部匹配曲线添加到你的模型后，最重要的就是通过移动，旋转，缩放定位器使匹配曲线尽量匹配到你的模型。

(具体操作时，我们可以先直接缩放匹配曲线的整体大小，然后再通过打开匹配曲线的不同层级来进一步整体控制匹配曲线的具体位置。

)做好这些后，我们只需要确定模型中的哪些部分将会作为面部处理，（或者作为眼睛，牙齿或舌头处理）然后保存我们的文件，并且点击“Rig”。

好啦，几秒后我们的模型就装配成功啦！（以上只是操作概述，具体的操作过程在下面）第零步：确定我们的模型是否标准。

（懂模型流程的可以跳过此步骤）我们的插件是很强大的，它能装配几乎所有的表情。

然而一个好的装配工作首先是从一个好的模型开始的，一个合格的符合流程的模型能使我们的装配工作变得更和谐。

就此我们提一些建模的意见，请笑纳：0）脸部和舌头模型必须是多边形，当然眼球和牙齿不是多边形没关系，还有模型中不能有子父级关系奥！1）模型尽量是环形边组成的，尤其是眼眶和嘴巴周围的线最好是一圈圈顺着结构走的，这样将更方便控制表情，你懂得！2）我们的模型要有足够多的面，（但别太多啦！）3）使我们的模型尽量保持四边面。

Crafter’s Choice Confetti Cake Fragrance Oil 7820

Product: Crafter’s Choice™ Confetti Cake Fragrance Oil7820 E Pleasant Valley RdIndependence, OH 44131(800) 359-0944 Page 1 of 3 2023-04-04 IndiMade Brands, LLC certifies that the above-mentioned fragrance product is in compliance with the standards of the International Fragrance Association [IFRA 50th Amendment (June '21)], provided the fragrance is used in the following application(s) at the following maximum concentration level(s):Product: Crafter’s Choice™ Confetti Cake Fragrance Oil7820 E Pleasant Valley RdIndependence, OH 44131(800) 359-0944 Page 2 of 3 2023-04-04Product: Crafter’s Choice™ Confetti Cake Fragrance Oil7820 E Pleasant Valley RdIndependence, OH 44131(800) 359-0944 Page 3 of 3 2023-04-04For all other applications, or use at higher concentration levels, a new evaluation will be required.The IFRA standards regarding use restrictions are based on safety assessments by the Research Institute for Fragrance Materials (RIFM) Expert Panel (REXPAN) and are enforced by the IFRA Scientific Committee. Evaluation of individual fragrance materials is made according to the safety standards contained in the relevant section of the IFRA Code of Practice.It is the ultimate responsibility of the customer to ensure the safety of the final product containing this fragrance, by further testing, if necessary.The above-mentioned fragrance product contains ingredients which are NOT considered GRAS, Generally Regarded as Safe as a Flavor Ingredient.。