Face recognition using the second-order mixture-of-eigenfaces method

The well-known eigenface method uses an eigenface set obtained from principal component analysis. However, the single eigenface set is not enough to represent the complicated face images with large variations of poses and/or illuminations. To overcome this weakness, we propose a second-order mixture-of-eigenfaces method that combines the second-order eigenface method (ISO MPG m5750, Noordwijkerhout, March 2000) and the mixture-of-eigenfaces method (a.k.a. Gaussian mixture model (Proceedings IJCNN2001, 2001). In this method, we use a couple of mixtures of multiple eigenface sets: one is a mixture of multiple approximate eigenface sets for face images and another is a mixture of multiple residual eigenface sets for residual face images. Each mixture of multiple eigenface sets has been obtained from expectation maximization learning consecutively. Based on two mixture of multiple eigenface sets, each face image is represented by a couple of feature vectors obtained by projecting the face image onto a selected approximate eigenface set and then by projecting the residual face image onto a selected residual eigenface set. Recognition is performed by the distance in the feature space between the input image and the template image stored in the face database. Simulation results show that the proposed second-order mixture-of-eigenfaces method is best for face images with illumination variations and the mixture-of-eigenfaces method is best for the face images with pose variations in terms of average of the normalized modified retrieval rank and false identification rate.     

基于奇异值的具有年龄变化的人脸识别

为了增强现有人脸识别算法对年龄变化的鲁棒性,提出了一种新的基于奇异值分解(SVD)和嵌入式隐马尔可夫模型(EHMM)的人脸识别方法.先选取整幅人脸图像的奇异值作为基本特征向量,然后建立年龄函数,对奇异值特征进行修正,再根据得到的年龄函数,对人脸图像进行重建,提取改进后的奇异值特征作为观察序列,送入EHMM中进行分类识别,实验结果表明这种方法能够提高具有年龄变化的人脸识别效率.     

二阶分块PCA的人脸特征提取方法研究

为了提取更为有效的鉴别特征,在已有的二阶特征脸方法和分块主成分分析(PCA)方法上,提出了二阶分块PCA人脸特征提取方法.该方法对原始人脸图像和经重建得到的剩余图像分别运用分块PCA,将提取的一阶和二阶特征线性组合为一个特征矩阵,再进行分类识别.此特征能更充分反映人脸图像的低频和高频特性.采用ORL人脸库和FERET人脸库的实验结果表明该二阶分块PCA正确识别率优于普通分块PCA算法,具有较强的特征提取能力.     

基于PCA余像空间的ICA混合特征人脸识别方法

为改善传统的基于特征脸的人脸识别方法在识别光照变化较大的人脸时效果不尽理想的缺陷,提出一种基于“PCA余像空间”的ICA混合特征人脸识别方法。不同于2阶PCA人脸识别方法,用独立元分析法代替主元分析法,对“PCA余像特征脸集”进行独立元特征抽取得到人脸图像基于PCA余像空间的独立元特征,并综合人脸图像的原始独立元特征得到混合特征作为最终识别的特征。实验表明,基于PCA余像空间的ICA混合特征人脸识别方法,在识别光照、表情等外界因素变化较大的人脸图像时,要优于传统的基于特征脸的识别方法、基于ICA的识别方法以及基于2阶PCA的人脸识别方法,并具有较强的适用性。     

一种基于核主成分特征组合的人脸识别方法

提出了一种基于核主成分特征组合的人脸识别方法。首先利用主成分分析,获得原始输入图像的二阶特征脸图像;然后运用核主成分分析分别抽取原始图像和二阶特征脸图像的核主成分特征,最后将它们组合成一个组合特征向量,进行人脸识别。在ORL人脸库上的实验表明,两种图像的核主成分特征分别有着良好的特点,取得了较好的识别效果,优于核主成分分析和二阶特征脸的结果。     

Facial expression recognition using ASM-based post-processing technique

Facial expression recognition is a challenging field in numerous researches, and impacts important applications in many areas such as human-computer interaction and data-driven animation, etc. Therefore, this paper proposes a facial expression recognition system using active shape model (ASM) landmark information and appearance-based classification algorithm, i.e., embedded hidden Markov model (EHMM). First, we use ASM landmark information for facial image normalization and weight factors of probability resulted from EHMM. The weight factor is calculated through investigating Kullback-Leibler (KL) divergence of best feature with high discrimination power. Next, we introduce the appearance-based recognition algorithm for classification of emotion states. Here, appearance-based recognition means the EHMM algorithm using two-dimensional discrete cosine transform (2D-DCT) feature vector. The performance evaluation of proposed method was performed with the CK facial expression database and the JAFFE database. As a result, the method using ASM information showed performance improvements of 6.5 and 2.5% compared to previous method using ASM-based face alignment for CK database and JAFFE database, respectively.     

基于PCA的人脸识别系统的研究与实现

人脸识别技术是人脸识别系统以人脸图像作为识别身份的媒介。研究了PCA算法的原理,基于matlab平台实现了人脸识别系统。该系统在对图像进行预处理的基础上,以ORL人脸库中部分图像为样本,采用K-L变换计算训练样本特征值及特征向量,得到特征脸向量,进而计算测试样本投影到特征脸空间的坐标系数,最后分类识别得出结果。实验证明该系统识别率较高,对实际应用有一定的参考价值。     

一种基于局部排序PCA的线性鉴别算法

主分量分析(Principal Component Analysis,PCA)是模式识别领域中一种重要的特征抽取方法,该方法通过K-L展开式来抽取样本的主要特征。基于此,提出一种拓展的PCA人脸识别方法,即分块排序PCA人脸识别方法(MSPCA)。分块排序PCA方法先对图像矩阵进行分块,对所有分块得到的子图像矩阵利用PCA方法求出矩阵的所有特征值所对应的特征向量并加以标识;然后找出这些所有的特征值中k个最大的特征值所对应的特征向量,用这些特征向量分别去抽取所属的子图像的特征;最后,在MSPCA的基础上,将抽取子图像所得到的特征矩阵合并,把这个合并后的特征矩阵作为新的样本进行PCA+LDA。与PCA和PCA+LDA方法相比,分块排序PCA由于使用子图像矩阵,可以避免使用奇异值分解理论,从而更加简便。在ORL人脸库上的实验结果表明,所提出的方法在识别性能上明显优于经典的PCA和PCA+LDA方法。     

基于特征块主成分分析的人脸表情识别

使用特征块主成分分析(FBPCA)对人脸表情进行特征提取,然后输入最近邻分类器得到分类结果.同时,讨论了特征脸个数在实验中的影响,总结出了识别率与特征脸数量的关系.分别在JAFFE表情库上做了训练数据包含测试数据和训练数据不包含测试数据等情况的表情识别.     

Example-based learning for view-based human face detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face” and “nonface” model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector measurements, whether or not a human face exists at the current image location. We show empirically that the distance metric we adopt for computing difference feature vectors, and the “nonface” clusters we include in our distribution-based model, are both critical for the success of our system     

基于Gabor系数分块统计和自适应特征选择的人脸描述与识别

提出一种新的人脸描述及识别方法,首先对归一化后的人脸图像进行多方向多尺度Gabor变换;然后对人脸区域进行分块,以块为单位统计Gabor系数的均值和方差,求得块特征矢量(block feature vector,BFV),按先行后列的顺序将各块的BFV拼接,构成整幅人脸图像特征矢量(face feature vector,FFV).在分类器设计阶段,引入两两比对和投票机制,用多个两类分类器组合成多类分类器.在训练某个具体的两类分类器时,根据隶属训练样本计算FFV中每项的分辨力,以分辨力大小为依据选出最优特征子集(best subset feature vector,BSFV).基于Yale人脸数据集展开实验,与已发表的算法和结果进行对比,证明了该方法的有效性.     

基于EHMM的人脸识别算法

人脸识别是对从视频图像中检测到的人脸区域进行身份的认证．是将待识别人脸与数据库中的人脸进行匹配的过程。将EHMM应用于人脸识别,提取人脸的DCT系数特征作为观察向量,用EHMM算法进行人脸模型训练和识别,并使用OpenCV对人脸识别算法进行功能仿真验证和相关探究,达到较好的人脸识别效果。实验结果表明,正常光照下,该算法的识别率在95％以上。     

基于肤色信息马氏距离图的人脸检测

提出了一种基于肤色信息的人脸检测方法。构造了皮肤颜色模型、检测图像的皮肤区域,并计算出该区域相对于皮肤的马氏距离图。根据马氏距离图寻找特征脸子空间,构造出BP神经网络。以马氏距离图在各特征脸子空间的投影系数为BP神经网络的输入,计算该区域是否为人脸图像。实验证明,该方法是有效的。     

基于2DPCA和压缩感知的人脸识别方法

提出一种基于二维主成份分析(2DPCA)和压缩感知的人脸识别方法.阐述2DPCA提取特征向量的工作原理,利用压缩感知方法求解待识别图像在足够样本下的稀疏表示.由所有训练图的特征向量构成测量矩阵,将每一幅待识别图像的特征向量作为测量值,由压缩感知中求解的L1范数极小值得到待识别图像的编码信号,根据该编码信号识别人脸图像....     

分形编码在人脸识别中的应用

利用人脸固有的对称特性,提出了基于对称性与方差的快速分形人脸图象压缩算法.对于一值域块,与其匹配的定义域块被限定在其对称区域中,从而可加快编码速度.本文分析了分形码的特点,定义了图象间分形码距离,进而提出了基于分形码的人脸识别算法,并对该算法的时间复杂性作了分析.通过在ORL和YALE人脸库上的实验结果表明,本文提出的识别算法的识别率分别可达到88%和92%,与本征脸方法基本持平,比直接采用基于方差的分形编码方法要高,且本文算法在人脸有装饰物情况下具有一定的稳定性,实验还对识别算法中参数的取值作了分析.     

基于特征运动的表情人脸识别

人脸像的面部表情识别一直是人脸识别的一个难点，为了提高表情人脸识别的鲁棒性，提出了一种基于特征运动的人脸识别方法，该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量，然后利用主成分分析方法（PCA）从这些运动向量中，产生低维子空间，称之为特征运动空间，测试时，先将测试人脸与无表情人脸之间的运动向量投影到特征运动空间，再根据这个运动向量在特征运动空间里的残差进行人脸识别，同时还介绍了基于特征运动的个人模型方法和公共模型方法，实验结果证明，该新算法在表情人脸的识别上，优于特征脸方法，有非常高的识别率。     

Face recognition using difference vector plus KPCA

In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.     

A real-time face detection and recognition system for a mobile robot in a complex background

This article presents a real-time face detection and recognition system for mobile robots based on videos with a complex background. In the visual system, we propose a multi-information method consisting of an Adaboost algorithm, and color information for the face detection part. The interesting targets in the video will first be detected by the Adaboost algorithm, which is robust to illumination. Then the skin color model in YCbCr space will be employed to select the parts that may not be skin areas from the information detected by the Adaboost algorithm. An embedded hidden Markov model (EHMM) is presented, using a 2-DCT feature vector as the observation vector, to recognize the faces detected. The whole process of detecting and recognizing a frame, which is 320 × 240, will take 1.4 s with the rapid recognition parameters and 4.2 s with the slow recognition parameters.     

基于综合特征的图像分割

文章提出了一种有效的基于颜色和纹理综合特征的图像分割方法。将图像以块为单位进行划分,在YUV空间,提取块的颜色特征和纹理特征,在这种综合特征基础上,采用改进的K均值聚类法进行图像分割。该方法能自适应确定聚类中的参数,且兼顾点的位置连通关系,从而达到了较好的分割效果。     

一种基于模糊支持向量机的人脸识别新算法

提出了基于独立分量分析进行特征提取和采用模糊支持向量机实现分类的人脸识别新方法。首先利用独立分量分析方法构造人脸的特征脸空间,在特征脸空间上运用模糊支持向量机进行分类识别。在ORL人脸数据库的仿真结果表明,该算法能有效提高人脸识别性能,具有较高的识别率。