人脸识别英文专业词汇教学提纲
人脸识别幻灯片讲义
将人脸识别这一多类问题转化为判断每一对 图像为类内 (同一个人 )还是类间 (不同人 )图像 的两类问题 。 利用局部特征而非全局特征进行识别 。 利用boosting的方法挑选出局部特征并构造 分离器。 分类器采用 cascade结构来解决类间样本过 多，无法一次全部参与训练的问题。
v v
v
相似度函数
v 基于贝叶斯区分特征的方法等
Alignment 的主流方法是ASM(Active Shape Models)
人脸识别的研究历史及现状
方法 几何特征 优点 比较直观 缺点 特征点的定位准确度要求 高 ，计算量大 计 算 量 大 ，对环境变化的 适应性较差 要 求 对 齐 准 确 度 高， 成像 条件不能有较大变化 对单训练样本集合无法计 算类内扩散矩阵 要求对齐准确度高 计算量大 网 络 结 构 、参数调整比较 复 杂；
题
纲
模板匹配 主成分分析 线性判别分 析 局部特征分 析 弹性模板匹 配 神经网络
成像条件不变的情况下效 果较好 方 便、易于实现 利 用 了 类 别 信 息， 效果较 好 ，尤适用于多训练样本 将局部与整体特征结合， 已在实际中应用 效果较好 ，能适应一定程 度的光线和角度变化 学习能力较强
１． 人脸识别简介 ２． 研究目的与系统框架 ２． 研究目的与系统框架 ３． 基于 基于ＡｄａＢｏｏｓｔ ＡｄａＢｏｏｓｔ的实时人脸检测 的实时人脸检测 ４． 基于弹性模板匹配的特征点定位 ５． 基于 ５． 基于Ｂｏｏｓｔｅｄ Ｌｏｃａｌ Ｆｅａｔｕｒｅｓ Ｂｏｏｓｔｅｄ Ｌｏｃａｌ Ｆｅａｔｕｒｅｓ人脸识别 人脸识别 ６． 基于三维可形变模型的人脸识别 ６． 基于三维可形变模型的人脸识别 ７． 已完成的工作和以后的工作 ７． 已完成的工作和以后的工作
【英语教案】面容特征：眼睛、鼻子和嘴巴
【英语教案】面容特征：眼睛、鼻子和嘴巴Introduction:人们一直在探索面容特征的神秘之处。
在面容特征中，眼睛、鼻子和嘴巴被认为是最重要和最基本的标志。
这些特征不仅赋予了面部美感，还在某种程度上反映了一个人的性格、健康状况、文化背景和地域特征。
因此，本教案将着重探讨人的面容特征及其各自的特点，旨在提高学生的英语口语水平、拓宽视野、增长知识。
Teaching Objectives：1. 了解人面部特征的基本构成要素，包括眼睛、鼻子和嘴巴。
2. 学会描述不同面部特征，根据面部特征判断不同人的年龄、性别和文化习俗等。
3. 通过教授听、说、读、写的方式，提高学生的英语口语能力。
Teaching Procedures：Step 1. 教师引入话题，引导学生探索不同的人面部特征T: Good morning, students. Let’s start today’s class by talking about someone’s face. Could you tell me what kind of face features can you distinguish whe n you look at someone’s face? (学生回答：eyes, nose, mouth, chin等)T: Very good. So, as you all know, eyes, nose, and mouth are the most important features that make up the face. Next, we will learn more about these features.Step 2. 学习眼部特征描述及不同文化的眼部习俗T: Le t’s start with the first feature, eyes. Eyes are the window to the soul. They can convey emotions, express moods, and even reflect one's personality. Now, let's take a closer look at the structure of eyes and learn some vocabulary about eyes.(present some relevant pictures, for example, the structure of the eye, the eyelid, the eyelash, the iris, the pupil, etc.)T: Great. Now we know what the eye is made of, next let's talk about the different cultural standards of eye beauty. For example, in some Asian countries, big eyes are consideredbeautiful, whereas in some European countries, almond-shaped eyes are more popular. Do you know any other eye cultural standards?(学生们举手回答，教师梳理总结）T: Since different countries have different cultural standards for eyes, do you think these standards affectpeople's lives and emotions? Please share your thoughts.Step 3. 学习鼻部特征描述及不同文化的鼻部习俗T: Good job, students. Now, let's move on to another important feature, the nose. Though the nose is not the most noticeable feature, it plays a vital role in overall facial appearance.(present some relevant pictures, such as the nasal bridge, the nostril, the nose tip, etc.)T: Now we have learned some vocabulary about the nose. Next, let's talk about the different cultural standards of nose beauty. For example, in many African countries, a broad and flat nose is considered attractive, while in someEuropean countries, a small and pointed nose is a sign of beauty. Do you know any other nose cultural standards?(学生们举手回答，教师梳理总结)T: Since different countries have different cultural standards for noses, do you think these standards affect people's lives and emotions? Please share your thoughts.Step 4. 学习嘴部特征描述及不同文化的嘴部习俗T: Well done, students. Now, let’s move onto the last important feature, the mouth. The mouth not only affects a person's facial features but also plays a significant role in their communication.(present some relevant pictures, such as the lips, the teeth, the cheeks, etc.)T: Now we have learned some vocabulary about the mouth. Next, let's talk about the different cultural standards oflip beauty. For example, in some African countries, a large, full-lipped mouth is considered attractive, while in someAsian countries, a small and delicate mouth is more popular. Do you know any other lip cultural standards?(学生们举手回答，教师梳理总结)T: Since different countries have different cultural standards for lips, do you think these standards affect people's lives and emotions? Please share your thoughts.Step 5. 总结课程，增强学生英语口语能力T: Great job today, students! We have learned a lot about facial features and cultural differences. Now, let’s summarize today's class. What have we learned today? Please share your opinions.(学生们回答，教师纠错并提供意见)T: As we have learned, facial features not only reflect our looks but also influence our identity, culture, andsocial status. Also, learning the cultural standards of beauty in different countries will help us understanddifferent cultures and customs better.T: OK. That's all for today's class. I hope you learned a lot and had fun. Homework tonight is to describe the face of a person you admire the most and its facial features in English. Have a great day, everyone!。
人脸识别介绍_IntroFaceDetectRecognition
Knowledge-based Methods: Summary
Pros:
Easy to come up with simple rules Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified Work well for face localization in uncluttered background
Template-Based Methods: Summary
Pros:
Simple
Cons:
Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledgebased methods)
Knowledge-Based Methods
Top Top-down approach: Represent a face using a set of human-coded rules Example:
The center part of face has uniform intensity values The difference between the average intensity values of the center part and the upper part is significant A face often appears with two eyes that are symmetric to each other, a nose and a mouth
人脸识别涉及到的知识
English Version:The Knowledge Behind Face Recognition TechnologyFace recognition is an advanced technology that has revolutionized the way we interact with digital systems. It involves a myriad of complex concepts and technologies, ranging from computer vision and artificial intelligence to biometric analysis. In this article, we will delve into the knowledge and principles behind face recognition.At its core, face recognition relies heavily on computer vision, a field that deals with the analysis and understanding of digital images and videos. Computer vision algorithms are trained to detect and identify patterns, such as edges, shapes, and textures, within these digital representations. In the context of face recognition, these algorithms are specifically designed to recognize and extract features from human faces.One of the most critical components of face recognition is feature extraction. This process involves identifying distinctive characteristics of a face, such as the shape of the eyes, nose, or mouth, and encoding them into a numerical representation. These features are then used to create a unique identifier for each individual, known as a faceprint.Faceprints are then compared against a database of pre-existing faceprints to identify a match. This comparison process is facilitated by algorithms that calculate the similarity between two faceprints based on various metrics. If a match is found, the system can then identify the individual associated with that faceprint.Artificial intelligence (AI) plays a crucial role in face recognition by enabling the system to learn and improve over time. Machine learning algorithms, such as deep neural networks, are trained on large datasets of faces to recognize patterns and make accurate identifications. As more data becomes available, these algorithms become more accurate and reliable.Biometric analysis is another important aspect of face recognition. Biometrics refers to the measurement and analysis of physical characteristics for identification purposes. In face recognition, biometric analysis involves measuring and comparing facial features to verify the identity of an individual.In conclusion, face recognition technology draws upon a vast array of knowledge and technologies, including computer vision, artificial intelligence, and biometric analysis. The complexity and sophistication of these components make face recognition a powerful and accurate tool for identification and verificationin various applications.Chinese Version:人脸识别背后的知识人脸识别技术已经彻底改变了我们与数字系统的交互方式，是一项革命性的技术。
LearningRacefromFace资料
4.1 Single-Model Race Recognition
• 由于数据收集和计算负担，现在的研究一般集中在基于单一模型的种族分类。
4.1.1 Iris Texture
• 虹膜识别是一种很常见的生物识别方式，并且虹膜的纹理被证明与种族有关； • 由于虹膜纹理获取的复杂性，基于虹膜纹理的种族识别仅仅只是理论研究。
4.1.2 Periocular Region
• 眼周区域（包括眼睑，睫毛，眼角或内眦等等）的数据更容易获得。 • 眼周部位可用来种族分类的观点越来越多，而且很多都已经得到证实。 • 运用睫毛的方向分类用来分类东亚、高加索人种的精度为93%。
东亚
非洲人
白人
印度人
4.1.3 3D Faces
• 基于2D脸部的种族分类通常会遇到图片几何失真和光照问题，而3D图片的 话就很少会有这些问题；
• 与全局特征不同,局部特征的每一维都只对应人脸图像上的一个局部区域， 因此侧重于提取人脸的细节特征。 • 这种方式被认为是可靠的、稳健的外观特征描述，中国人基于这种方式 建立了一个模型，成功的分类识别了中国的不同的的少数民族。
4 . RACE CLASSIFICATION: STATE-OF-THE-ART
3. 1 Overview
• 为了模拟人类视觉系统是如何识别种族的，分类人种第一步是找到 一种特征标识，早期的工作是基于从一个人种的脸上提取颜色，纹理， 几何形状等来建立一个模型，虽然这种方式很简单也很有效率，但是这 种方式往往表现不佳。
3.2 Racial Feature Representation
• 人体测量学的研究已经开始使用3D人体测量来分类种族和进行相关的面部 分析；
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。
Facial Recognition--人脸识别
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
人脸识别
5.1. Databases and normalization procedure
6. Conclusions
In this paper, we have presented a new technique for face recognition, which uses the combination of Radon transform and DWT to derive the directional spatial frequency features. The low-frequency components, which play significant role in the identification process, are amplified and recognition rate is improved. The DWT derives the multiresolution features from the Radon space. The paper has also compared the performances of the different face recognition algorithms in terms of changes in facial expression and illumination. With the proposed approach, the recognition rates (for normal images) based on FERET, ORL, Yale, and YaleB databases are 97.33%, 96%, 100%, and 100%, respectively.
R(r , )[
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
人脸识别英文专业词
汇
gallery set参考图像集
Probe set=test set测试图像集
face rendering
Facial Landmark Detection人脸特征点检测
3D Morphable Model 3D形变模型
AAM (Active Appearance Model)主动外观模型Aging modeling老化建模
Aging simulation老化模拟
Analysis by synthesis 综合分析
Aperture stop孔径光标栏
Appearance Feature表观特征
Baseline基准系统
Benchmarking 确定基准
Bidirectional relighting 双向重光照
Camera calibration摄像机标定（校正）Cascade of classifiers 级联分类器
face detection 人脸检测
Facial expression面部表情
Depth of field 景深
Edgelet 小边特征
Eigen light-fields本征光场
Eigenface特征脸
Exposure time曝光时间
Expression editing表情编辑
Expression mapping表情映射
Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化
Eye localization,眼睛定位
face image acquisition 人脸图像获取
Face aging人脸老化
Face alignment人脸对齐
Face categorization人脸分类
Frontal faces 正面人脸
Face Identification人脸识别
Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪
Facial action coding system面部动作编码系统
Facial aging面部老化
Facial animation parameters脸部动画参数
Facial expression analysis人脸表情分析
Facial landmark面部特征点
Facial Definition Parameters人脸定义参数
Field of view视场
Focal length焦距
Geometric warping几何扭曲
Street view街景
Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥
Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型
Light-field光场
Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择
Capture systems获取系统
Frontal lighting正面光照
Open-set identification开集识别Operating point操作点
Person detection行人检测
Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化
Pose correction姿态校正
Privacy concern隐私关注
Privacy policies隐私策略
Profile extraction轮廓提取
Rigid transformation刚体变换
Sequential importance sampling序贯重要性抽样
Skin reflectance model,皮肤反射模型
Specular reflectance镜面反射
Stereo baseline 立体基线
Super-resolution超分辨率
Facial side-view面部侧视图
Texture mapping纹理映射
Texture pattern纹理模式
Rama Chellappa
读博计划：
1.完成先前关于指纹细节点统计建模的相关工作。
研究生期间，我参与了指纹细节点统计建模相关的研究工作，我们提出了一个非参回归模型用于量化指纹细节点空域分布的非齐次性，工作于2015年发表在国际生物特征识别大会（ICB），后来做了些扩展工作，关于细节点模型评估方法的比较和研究。
2.深入学习关于深度学习的相关理论和应用实践，并希望从事相关的研究工作。
当前深度学习应用越来越很广泛，实际效果较传统方法有很大改进，显示了非常好的前景。
但深度学习的理论研究显得相对落后，关于深层神经网络模型的参数设置和训练还只停留在比较经验性的指导原则上，对于非专家的广大应用工作者来说，很难掌握。
我有幸参加了2015年的国际身份安全和行为分析国际会议，当时马里兰大学的Rama Chellappa作为大会主席（他也是今年cvpr的大会主席），他说到当前很多应用表明深度学习非常成功，但缺少理论基础，对于一个博士生来说，这是一个很有挑战性的研究课题。
多少受到他的影响，但从我个人来说，我有着强烈欲望，想搞清楚why and how does it work? 因此，希望从事相关的学习和研究工作。