一种基于多视点图像的可变姿态人脸识别系统

本文报告了一种多姿态人脸图象识别原型系统，它不同于现有系统和方法，该系统可工作在合作对象下允许姿态变化（存在图象平面内旋转和深度方向上旋转，限于双眼可见）的人脸图象识别。由于对成象条件有所放松，故可望应用于身份验证、保安和视频付领域。对姿态可变条件下的人脸特征检测、姿态估计、识别建模以及基于模板相关的匹配等技术进行了深入研究，分析了光照、姿态及分辨率变化等地识别的影响程度。实验结果表明，对于３０类     

基于最佳鉴别变换和分类器组合的人脸自动识别

人脸识别技术在商业和法律上有广泛的应用前景 ,在安全监控中也大有用武之地 .其主要任务是利用已有的人脸图象库 ,识别静止的或视频图象中的一张或多张人脸 .从抽取具有统计不相关的模式特征着手 ,通过基于小波变换的图象分解和 KL 变换等处理 ,避开人脸识别的小样本集的局限 ,并通过运用具有统计不相关性的最佳鉴别变换 ,来抽取人脸的有效鉴别特征 .同时 ,利用多特征多分类器组合的方法对图象进行识别 .该方法在 ORL 人脸图象库上进行实验 ,得到识别错误率为 2 %的实验结果 ,这是目前在此人脸图象数据库上所得到的最好的实验结果 .而且本方法对人脸的姿态、表情等条件具有一定的不敏感性     

基于身份子空间统一模型的人脸识别技术

人脸识别与许多其他的传统模式识别问题具有明显的区别。一方面，在人脸识别系统中，经常有成百上千的非常大量的类数(人数)，而对于每个人却只有很少的几幅图象，甚至每人只有一个图象样本的情况也屡见不鲜。另一方面，人脸识别还受到光照、姿态、表情、年龄、图象质量、图象尺寸、背景等因素的影响。本文的研究主要针对在光照、姿态、表情等因素的影响下、每人只有单幅图象的大规模(几百人以上)的基本正面人脸图象的识别问题。本文的主要贡献和创新点包括：     

降维方法在人脸图像识别中的分析与评估

为了提高人脸图像识别的速度和准确率,将多元统计分析中的降维方法引入到人脸图像识别系统中,分析并对比了主成分分析法（PCA）线性判别分析法（LDA）和局部保留投影（LPP）三种方法用于人脸图像识别的效果,采用YaIe B作为识别的样本和测试库,在光照条件和姿态变化条件下对三种算法进行了比较实验。实验结果表明,三种方法对人脸图像识别均能够达到一定效果,在姿态变化条件下,LDA和LPP均优于PCA,但三种方法对于光照变化均不能保持很好的鲁棒性。     

人脸特征的定位与提取

人脸的特征定位与特征提取的好坏对于人脸图象识别效果有直接的影响．本文根据人脸图象的灰度特性以及特征的几何形状提出了一种简便有效的特征定位和特征提取的方法。本文所提出的方法能够比较准确而快速的提取较多的特征，因而能够适用于在大量人像中进行特征提取和识别。     

贝叶斯模型下基于SIFT特征的人脸识别

根据姿态与表情变化对人脸识别的影响,采用对图像的旋转、尺度变化保持不变性的SIFT算子作为人脸特征,建立人脸各个子区域的相似性测度,并通过混合高斯建立不同变形条件下相同样本与不同样本的相似性概率模型。在此基础上,利用各子区域特有的识别能力获取子区域概率权值,结合基于贝叶斯公式建立的概率框架确定识别结果。实验结果表明,与直接用SIFT算子进行人脸识别的方法相比,该方法在姿态变化较大及表情变化较大的情况下识别率有明显提高。     

基于奇异值特征和隐马尔可夫模型的人脸检测

提出了基于奇异值特征和隐马尔可夫模型（HMM）的人脸检测方法,首先提出了基于奇异值特征和隐马尔可夫模型的正面端正人脸检测方法;然后将该算法扩展到检测任意旋转角度的人脸,其中正向端正人脸检测算法是通过隐马尔可夫模型来识别人脸／非人脸的奇异值特征,从而达到人脸检测的目的;扩展算法首无计算当前位置子图象窗口的奇异值特征向量,然后利用识别各个旋转角度人脸的HMM模型对之进行分类,以得到该子图象窗口的旋转角度,再经过旋正,重新再与识别正面端正人脸的HMM模型对, 此确定该子图象窗口是否为人脸,通过对一个由51幅集体照片组成的图象集进行测试,其中,正面端正人脸检测率为85．1％,而任意旋转角度的人脸检测率只有72．2％。     

基于SVD的人脸对称性的两步人脸识别算法

为了缓解人脸图像容易受光照、表情和姿态变化对人脸识别的影响, Yong提出了利用了人脸的对称性产生新的样本来表示人脸特征的方法.这种方法可以反映出人脸样本由于表情、姿态等外在因素引起的变化,一定程度上提高识别效果.但是当样本受外在因素影响产生较大变化时, Yong的方法的识别结果并不理想.而奇异值分解对光照等外在条件引起的灰度变化不敏感,可以缓解人脸对称性在人脸识别中的不足.因此作者在Yong提出的人脸对称性方法的基础上,分别采用SVD和图像镜像的方式构造一幅对称图像则可以缓解其方法中的不足.在ORL、FERET和UMIST三个人脸数据库上进行了重构和识别的实验,并证明了改进算法在人脸重构和识别方面具有明显的优势.     

小样本条件下基于随机森林和Haar特征的多姿态人脸识别

针对小样本环境下存在人脸姿态、表情变化等干扰时的人脸识别问题,提出利用基于Haar特征的随机森林分类器完成对注册样本和待识别人脸图像的关键点自适应定位,再以SURF(Speed-Up Robust Features)特征的欧氏距离决策得出初匹配和再匹配关键点,完成人脸识别,解决在小样本环境下识别多姿态人脸图像的问题。实验结果证明,该方法在表情、姿态变化等干扰情况下能有效提高小样本人脸识别的识别率。     

基于在线特征选择的实时多姿态人脸跟踪

针对复杂条件下的人脸跟踪问题, 将显著区域跟踪算法和基于 Adaboost 的人脸检测算法相结合, 研发了一个实时多姿态人脸跟踪系统. 系统采用数据关联结果, 自动选择和切换检测器与跟踪器, 并通过引入环境信息增强跟踪算法的稳定性. 实验表明, 系统可在目标姿态变化、摄像机运动等复杂条件下进行自动人脸检测与跟踪, 对 320x240 的图像序列处理速度达到 10-12帧/秒.     

Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models

This paper presents a multimodal system for reliable human identity recognition under variant conditions. Our system fuses the recognition of face and speech with a general probabilistic framework. For face recognition, we propose a new spectral learning algorithm, which considers not only the discriminative relations among the training data but also the generative models for each class. Due to the tedious cost of face labeling in practice, our spectral face learning utilizes a semi-supervised strategy. That is, only a small number of labeled faces are used in our training step, and the labels are optimally propagated to other unlabeled training faces. Besides requiring much less labeled data, our algorithm also enables a natural way to explicitly train an outlier model that approximately represents unauthorized faces. To boost the robustness of our system for human recognition under various environments, our face recognition is further complemented by a speaker identification agent. Specifically, this agent models the statistical variations of fixed-phrase speech using speaker-dependent word hidden Markov models. Experiments on benchmark databases validate the effectiveness of our face recognition and speaker identification agents, and demonstrate that the recognition accuracy can be apparently improved by integrating these two independent biometric sources together.     

Face recognition using line edge map

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition     

Preserving privacy by de-identifying face images

In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers' license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by deidentifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to deidentifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This work presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize deidentified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.     

Homomorphic filtering based illumination normalization method for face recognition

The appearance of a face image is severely affected by illumination conditions that will hinder the automatic face recognition process. To recognize faces under varying lighting conditions, a homomorphic filtering-based illumination normalization method is proposed in this paper. In this work, the effect of illumination is effectively reduced by a modified implementation of homomorphic filtering whose key component is a Difference of Gaussian (DoG) filter, and the contrast is enhanced by histogram equalization. The resulted face image is not only reduced illumination effect but also preserved edges and details that will facilitate the further face recognition task. Among others, our method has the following advantages: (1) neither does it need any prior information of 3D shape or light sources, nor many training samples thus can be directly applied to single training image per person condition; and (2) it is simple and computationally fast because there are mature and fast algorithms for the Fourier transform used in homomorphic filter. The Eigenfaces method is chosen to recognize the normalized face images. Experimental results on the Yale face database B and the CMU PIE face database demonstrate the significant performance improvement of the proposed method in the face recognition system for the face images with large illumination variations.     

Facial expression analysis and expression-invariant face recognition by manifold-based synthesis

Over the last decades, expression classification and face recognition have received substantial attention in computer vision and pattern recognition with more recent efforts focusing on understanding and modelling expression variations. In this paper, we present an expression classification and expression-invariant face recognition method by synthesising photorealistic expression manifolds to expand the gallery set. By means of synthesising expression images from neutral faces, more within-subject variability can be obtained. Eigentransformation is utilised to generate both shape and expression details for novel subjects. Expression classification and face recognition are then performed on the extended training set with synthesised expressions. Experimental results on various datasets show that the proposed method is robust for recognising various expressions and faces with varying degrees of expression. Comprehensive experiments conducted and comparisons with the existing methods are reported. Cross-database synthesis and effect of landmark quality are also studied.     

A face detection and recognition system in color image series

A human face detection and recognition system for color image series is presented in this paper. The system is composed of two subsystems: human face detection subsystem and human face recognition subsystem. The face detection subsystem includes two modules: face finding and face verification. The human face finding module determines the face regions of a number of subjects from color image series using skin color analysis and motion analysis. The human face verification module is developed to verify the detected human faces by judging of eclipse and support vector machine (SVM), and precisely localize human faces by locating eyes and mouths based on Generalized Symmetry Transform. The features characterizing the relation between face patterns can be extracted and selected by Principal Component Analysis. Using these selected features to train multiple SVMs, we can finally classify human faces. Moreover, in these modules, several simple and complex methods are used to reduce the searching space. So the system can work at a high speed and high detection and recognition rate. Human face detection accuracy of the system is 97.2% under controllable lightning condition. Human face recognition accuracy of the system for 70 persons is 96.5% (with 20 eigenvectors) and 98.3% (with 30 eigenvectors).     

视频监控下的人脸跟踪与识别系统

针对智能视频监控系统的要求,设计了一个基于视频监控的自动多人脸跟踪识别系统,该系统的功能是实时跟踪视频监控范围内的人脸并鉴别人脸的身份。针对复杂背景及类似人脸区域的影响,提出了一种Adaboost人脸检测算法和主动形状模型相结合的人脸检测算法,实现人脸的准确检测;针对视频监控范围内人脸偏转、交错以及由于人员不断出入而导致人脸数目发生变化的问题,提出了CamShift和Kalman滤波器相结合的多人脸跟踪算法,同时对跟踪到的人脸进行实时身份识别。实验证明,该系统在视频监控范围内对人脸检测和身份识别准确,跟踪实时性好,是一种建立实时视频监控系统的实用方法。     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

Illuminant-Dependence of Von Kries Type Quotients

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods.The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.     

基于因子分析与稀疏表示的多姿态人脸识别

在非可控环境下,人脸识别面临的最大难题之一是姿态变化与遮挡问题。基于稀疏表示的人脸识别方法将测试人脸表示成训练人脸的稀疏线性组合,根据其组合系数的稀疏性进行人脸识别。该方法对人脸的噪声和遮挡变化具有很好的鲁棒性,但对人脸的姿态变化表现力极差,这是因为当人脸具有姿态变化时,同一个人不同姿态情况下很难对应起来,这违背线性组合的前提条件。为了克服稀疏表示方法对人脸姿态变化表现力极差问题,对人脸进行因子分析,分离出人脸姿态因子,得到合成的正面人脸;利用稀疏表示进行人脸分类识别。实验结果表明,该方法对人脸的遮挡和姿态变化具有很好的鲁棒性。