Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count

Face recognition has a wide range of possible applications in surveillance, human computer interfaces and marketing and advertising goods for selected customers according to age and gender. Because of the high classification rate and reduced computational time, one of the best methods for face recognition is based on Gabor jet feature extraction and Borda count classification. In this paper, we propose methodological improvements to increase face recognition rate by selection of Gabor jets using entropy and genetic algorithms. This selection of jets additionally allows faster processing for real-time face recognition. We also propose improvements in the Borda count classification through a weighted Borda count and a threshold to eliminate low score jets from the voting process to increase the face recognition rate. Combinations of Gabor jet selection and Borda count improvements are also proposed. We compare our results with those published in the literature to date and find significant improvements. Our best results on the FERET database are 99.8%, 99.5%, 89.2% and 86.8% recognition rates on the subsets Fb, Fc, Dup1 and Dup2, respectively. Compared to the best results published in the literature, the total number of recognition errors decreased from 163 to 112 (31%). We also tested the proposed method under illumination changes, occlusions with sunglasses and scarves and for small pose variations. Results on two different face databases (AR and Extended Yale B) with significant illumination changes showed over 90% recognition rate. The combination EJS-BTH-BIP reached 98% and 99% recognition rate in images with sunglasses and scarves from the AR database, respectively. The proposed method reached 93.5% recognition on faces with small pose variation of 25° rotation and 98.5% with 15% rotation in the FERET database.     

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.     

Enhancing face recognition using Directional Filter Banks

Face recognition is an increasingly important problem in biometric applications; consequently many recognition algorithms have been proposed during the last three decades. It is accepted that the use of a pre-processing step can extract more discriminating features and increase the classification rates. Although, Gabor filters have been widely employed they do not provide satisfying classification results. This paper proposes the use of directional filters as a pre-processing step to demonstrate that a Directional Filter Bank is capable of enhancing existing face recognition classifiers such as PCA, ICA, LDA and SDA. The proposed method is tested using two different databases: the Yale face database and the FERET database. Experimental results demonstrate that the pre-processing phase enhances the classification rates. A comparative study has also been carried out to demonstrate that a DFB based classification outperforms a Gabor type one.     

基于多类最大散度差的人脸表示方法

将用于两类分类的最大散度差鉴别准则推广为多类最大散度差鉴别准则,并建立了基于该准则的一种新的人脸表示方法.基于多类最大散度差鉴别准则的人脸表示方法有效避免了传统鉴别分析方法在人脸特征提取时通常面临的小样本模式识别问题.在国际标准人脸图像数据库ORL、Yale以及FERET上的实验结果表明,与Fisherfaces、Eigenfaces、正交补空间、零空间等人脸特征提取方法相比,新的人脸表示方法具有一定的优势.     

Fusion of local normalization and Gabor entropy weighted features for face identification

Face recognition is one of the most extensively studied topics in image analysis because of its wide range of possible applications such as in surveillance, access control, content-based video search, human–computer interaction, electronic advertisement and more. Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database. In this work we propose two new methods for face identification. The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features. The second fuses the entropy-like weighted Gabor features at the score level with the local binary pattern (LBP) applied to the magnitude (LGBP) and phase (LGXP) components of the Gabor features. We used the FERET, AR, and FRGC 2.0 databases to test and compare our results with those previously published. Results on these databases show significant improvement relative to previously published results, reaching the best performance on the FERET and AR databases. Our methods also showed significant robustness to slight pose variations. We tested the proposed methods assuming noisy eye detection to check their robustness to inexact face alignment. Results show that the proposed methods are robust to errors of up to 3 pixels in eye detection.     

Supervised Discriminant Projection with Its Application to Face Recognition

In the past few decades, many face recognition methods have been developed. Among these methods, subspace analysis is an effective approach for face recognition. Unsupervised discriminant projection (UDP) finds an embedding subspace that preserves local structure information, and uncovers and separates embedding corresponding to different manifolds. Though UDP has been applied in many fields, it has limits to solve the classification tasks, such as the ignorance of the class information. Thus, a novel subspace method, called supervised discriminant projection (SDP), is proposed for face recognition in this paper. In our method, the class information was utilized in the procedure of feature extraction. In SDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and class information. We test the performance of the proposed method SDP on three popular face image databases (i.e. AR database, Yale database, and a subset of FERET database). Experimental results show that the proposed method is effective.     

基于Gabor特征与加权协同表示的人脸识别算法

针对基于稀疏表示分类方法的训练样本于与类别标签信息提取不足,特别是在训练样本和待测样本都受到噪声污染的情况下将会明显下降及算法复杂度较高的问题,提出以Gabor特征以及加权协同为基础的人脸识别算法;最初需要对人脸图像内所包含的各个尺度以及方向的Gabor特征完成提取,在稀疏表示中引入Gabor特征,将降维后的Gabor特征矩阵作为超完备字典,再用稀疏表示增强加权协同表示得到该字典下的的稀疏表示系数,然后利用增强系数与训练样本的标签矩阵完成对测试样本进行分类识别,从而得到Gabor特征以及加权的协同表示分类方法,在Yale人脸数据库、Extended Yale B和AR人脸数据库上以及在FERET人脸数据库对人脸姿态变化的实验表明新算法具有更好的识别率和较短的计算时间.     

一种基于模糊2DPLA的人脸识别方法

将模糊集的隶属度函数矩阵嵌入到二维主成分分析以及二维线性判别分析中,形成了一种基于模糊2DPLA的新方法。该方法首先通过基于模糊的KNN方法求出隶属度函数矩阵;然后将隶属度函数矩阵从图像矩阵的水平方向和垂直方向分别嵌入到二维主成分分析和二维线性判别分析中,从而更好地实现降维;最后采用基于矩阵的F-范数代替传统的基于向量的2一范数进行分类度量。实验阶段,采用Yale Face Database B, ORI和FERET人脸数据库进行了测试和验证。结果证明,该方法具有较好的鲁棒性,并能获得较高的识别率。     

权值正交重构判别分析算法

重构判别分析(Reconstructive Discriminant Analysis, RDA)算法未考虑线性子空间距离不同,导致较差的子空间数据结构保持能力,并且RDA算法是非正交的,给数据重构带来困难。针对这些不足,提出一种权值正交重构判别分析(Weighted Orthogonal Reconstructive Discriminant Analysis, WORDA)算法,该算法首先针对线性子空间距离不同引入线性子空间权值矩阵,以提高子空间数据结构保持能力;又在投影过程加上正交约束克服测度扭曲问题,获得更好的子空间投影。在ORL、Yale、AR和FERET人脸库上大量实验验证了该算法的有效性。     

一种监督的局部保持典型相关分析算法

从模式分类的角度出发,提出一种监督的局部保持典型相关分析(SLPCCA),通过最大类内成对样本与其近邻间的权重相关性,因而能有效利用样本类别信息的同时保持数据的局部流形结构,并且融合判别型典型相关分析(DCCA)的鉴别信息而不受总类别数的限制。此外,为了提取数据的非线性特征,在核方法的基础上又提出一种核化的SLPCCA(KSLPCCA)。在ORL、Yale、AR和FERET等人脸数据库的实验结果表明,该算法比其他传统的典型相关分析方法具有更好的识别效果。     

Face Recognition Using A Low Rank Representation Based Projections Method

In this paper, a low rank representation based projections (LRRP) method is presented for face recognition. In LRRP, low rank representation is used to construct a nuclear graph to characterize the local compactness information by designing the local scatter matrix like SPP; the total separability information is characterized by the total scatter like PCA. LRRP seeks the projection matrix simultaneously maximizing the total separability and the local compactness. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that LRRP works well for face recognition.     

An illumination,expression, and noise invariant gender classifier using two-directional 2DPCA on real Gabor space

This work investigates a framework for gender classification which is invariant to illumination, expression, and noise. It utilizes 2D Gabor filter along with two-directional 2DPCA. Gabor filter gives the real Gabor space which contains crucial face information and prohibits the illumination, expression, and deformation to a certain extent. The real Gabor space is high dimensional to represent face images. To select the most discriminate feature set from the real Gabor space, 2DPCA in horizontal and vertical directions is used. The proposed scheme is evaluated through a number of well-known face image databases. The system yields classification rate of 98.18%, 96.61%, 96.15%, 93.33%, and 88.34% for FERET, FIE A, AR, Indian Face, and LFW, respectively. In the presence of illumination, expression, and noise, the system gives more than 90% accuracy for all databases except LFW. The system does so with reduced features which make it suitable for real life applications.     

Feature extraction for classification problems and its application to face recognition

This study investigates a new method of feature extraction for classification problems. The method is based on the independent component analysis (ICA). However, unlike the original ICA, one of the unsupervised learning methods, it is developed for classification problems by utilizing class information. The proposed method is an extension of our previous work on binary-class problems to multi-class problems. It treats the class labels as input features in order to produce two sets of new features: one that carries much information on the class labels and the other that is irrelevant to the class. The learning rule for this method is obtained using the stochastic gradient method to maximize the likelihood of the observed data. Among the new features, using only class-relevant ones, the dimension of the feature space can be greatly reduced in line with the principle of parsimony, resulting better generalization. This method was applied to recognize face identities and facial expressions using various databases such as the Yale, AT&T (former ORL), Color FERET face databases and so on. The performance of the proposed method was compared with those of conventional methods such as the principal component analysis (PCA), Fisher's linear discriminant (FLD), etc. The experimental results show that the proposed method performs well for face recognition problems.     

SRC最佳鉴别投影及其在人脸识别中的应用

根据稀疏表示分类器的分类准则,提出了一种稀疏表示分类器最佳判别的投影方法。该方法优化两个目标,一是数据集的类间和类内稀疏重构误差,二是数据集中区分度。优化结果使样本投影到低维空间中,确保SRC具有更好的分类性能。在AR和Yale数据库上进行人脸识别实验,并与几种流行的方法进行了比较,结果表明所提出的方法具有良好的有效性和鲁棒性。     

核典型相关分析特征融合方法及应用

构建了一种基于核函数的典型相关分析的特征融合算法。首先,利用核函数将图像矩阵映射到核空间,再抽取同一模式的两组特征向量,在两组特征向量之间建立描述它们的相关性的判据准则函数;然后依此准则函数抽取两组典型投影矢量集;最后通过给定的特征融合策略抽取组合的典型相关特征以用于分类识别。该算法将两组特征向量之间的相关性特征作为有效鉴别信息,既可以很好地融合信息,又可以有效地去除特征之间的信息冗余,并且避免了对映射后的数据矩阵进行分解,从而简化了数据运算。在AR、PIE、ORL、Yale人脸数据库及UCI手写体数字库上的实验结果证明了该方法的有效性和稳定性。     

熵权法融合局部匹配Gabor特征的鲁棒人脸识别

针对复杂环境下人脸识别难度大的问题,提出了一种熵权法融合局部Gabor特征方法。计算类熵加权向量;计算局部归一化输入图像的Borda计数矩阵,从而消除低值Gabor jet比较矩阵;通过将分数层类熵加权Gabor特征与LGBP和LGXP融合解决了完成人脸的识别。在FERET、AR和FRGC 2.0人脸数据库上的实验结果表明,该方法对轻微姿态变化具有显著鲁棒性,并且对人眼检测中高达3像素的误差具有鲁棒性,相比其他几种人脸识别方法,该方法取得了更好的识别效果。     

Optimal subset-division based discrimination and its kernelization for face and palmprint recognition

Discriminant analysis is effective in extracting discriminative features and reducing dimensionality. In this paper, we propose an optimal subset-division based discrimination (OSDD) approach to enhance the classification performance of discriminant analysis technique. OSDD first divides the sample set into several subsets by using an improved stability criterion and K-means algorithm. We separately calculate the optimal discriminant vectors from each subset. Then we construct the projection transformation by combining the discriminant vectors derived from all subsets. Furthermore, we provide a nonlinear extension of OSDD, that is, the optimal subset-division based kernel discrimination (OSKD) approach. It employs the kernel K-means algorithm to divide the sample set in the kernel space and obtains the nonlinear projection transformation. The proposed approaches are applied to face and palmprint recognition, and are examined using the AR and FERET face databases and the PolyU palmprint database. The experimental results demonstrate that the proposed approaches outperform several related linear and nonlinear discriminant analysis methods.     

Class mean embedding for face recognition

Recently, local discriminant embedding (LDE) was proposed as a means of addressing manifold learning and pattern classification. In the LDE framework, the neighbor and class of data points are used to construct the graph embedding for classification problems. From a high dimensional to a low dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of different classes no longer stick to one another. But, neighboring data points of different classes are not deemphasized efficiently by LDE and it may degrade the performance of classification. In this paper, we investigate its extension, called class mean embedding (CME), using class mean of data points to enhance its discriminant power in their mapping into a low dimensional space. After joined class mean data points, (1) CME may cause each class of data points to be more compact in the high dimension space; (2) CME may increase the quantity of data points, and solves the small sample size (SSS) problem; (3) CME may preserve well the local geometry of the data manifolds in the embedding space. Experimental results on ORL, Yale, AR, and FERET face databases show the effectiveness of the proposed method.     

Feature extraction based on Laplacian bidirectional maximum margin criterion

Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples and the structural information embedding in the images. In this paper, we develop a novel criterion, namely Laplacian bidirectional maximum margin criterion (LBMMC), to address the issue. We formulate the image total Laplacian matrix, image within-class Laplacian matrix and image between-class Laplacian matrix using the sample similar weight that is widely used in machine learning. The proposed LBMMC based feature extraction computes the discriminant vectors by maximizing the difference between image between-class Laplacian matrix and image within-class Laplacian matrix in both row and column directions. Experiments on the FERET and Yale face databases show the effectiveness of the proposed LBMMC based feature extraction method.     

基于压缩金字塔核稀疏表示的人脸识别

人脸识别中光照、伪装及姿态等变化一直是富有挑战性的问题,其中特征提取是很关键的一步。为提高人脸识别率,结合压缩感知和空间金字塔模型,本文提出了一种新的特征提取方法,首先用尺度不变特征变换算法提取图像特征,然后与随机生成的字典进行稀疏编码,再用金字塔模型分层提取不同尺度空间的特征,并用最大池融合特征,最后运用核稀疏表示分类。在Extended Yale B,AR 和CMU PIE人脸数据库上的实验结果表明,该方法对于人脸图像的光照、伪装及姿态等变化有较强的鲁棒性,而且该算法有较快的运行速度。