Face recognition using Gabor-based direct linear discriminant analysis and support vector machine

This paper presents a novel and uniform framework for face recognition. This framework is based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and support vector machine (SVM). First, feature vectors are extracted from raw face images using Gabor wavelets. These Gabor-based features are robust against local distortions caused by the variance of illumination, expression and pose. Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique. The Gabor-based DLDA feature vectors are then applied to SVM classifier. A new kernel function for SVM called hyperhemispherically normalized polynomial (HNP) is also proposed in this paper and its validity on the improvement of classification accuracy is theoretically proved and experimentally tested for face recognition. The proposed algorithm was evaluated using the FERET database. Experimental results show that the proposed face recognition system outperforms other related approaches in terms of recognition rate.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

基于Gabor滤波器的快速人脸识别算法

针对传统人脸识别方法中所提取特征维数高、计算量大等缺点,提出一种新的正面人脸识别算法。新算法融合了半边人脸识别方法、Gabor滤波器、基于互信息判据的Gabor特征筛选来进行人脸识别。新算法将人脸图像分为左右两个部分,计算并比较人脸图像左右半边脸的熵,选取熵值较大的半边人脸图像进行Gabor特征提取。利用二值分类器判别单个Gabor特征的分类能力,选取分类能力较强的特征（最具判决力的特征）。再利用互信息判据对Gabor特征进行第二次筛选,以减小特征之间的冗余度。最后利用最近邻判别器来进行人脸识别。实验结果表明,新算法的识别率优于传统半边脸识别方法,识别速度也优于传统的利用Gabor滤波器进行特征提取的方法。     

Gabor-based kernel PCA with fractional power polynomial models for face recognition

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.     

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.     

基于彩色人脸图像的信息融合与识别方法

图像的彩色信息进行图像识别并有效地降低因利用颜色信息所带来的计算量大幅增加问题,提出了一种基于彩色图像的监督近邻保留嵌套的人脸识别方法,通过对图像的彩色信息进行信息融合并利用监督近邻保留嵌套算法来提高人脸识别的效率。首先,采用Gabor变换分别对彩色图像的每个彩色分量图提取Gabor特征;然后采用典型相关分析对所提取的Gabor特征进行特征融合,并采用监督近邻保留嵌套算法对高维彩色图像特征进行降维;最后,采用最近邻分类器对图像进行分类。实验基于XM2VTS和FRAV2D彩色人脸数据库,采用主成分分析、线性判别分析以及监督近邻保留嵌套对基于灰度图像的Gabor特征和基于彩色信息融合的Gabor特征进行降维,其结果说明多信通彩色图像融合技术与监督近邻保留嵌套结合的方法可以显著提高识别系统性能。     

结合空间显著性的融合单演二元模式面部情感分类

针对图像Gabor变换计算代价和存储空间开销较高的问题, 提出一种基于单演信号分析的人脸表情描述方法。该方法首先采用单演信号分析将人脸图像分解为单演幅度、相位和方向三个图像, 并将其划分为多个矩形块子区域; 然后在三幅图像的子区域上提取相应的由空间显著性加权的单演幅度、相位和方向二元模式特征直方图; 最后将结合了空间显著性的三个加权特征进行融合增强特征的可分辨性。在JAFFE人脸表情数据库上的实验结果表明, 该方法能有效提取人脸表情特征, 提高人脸表情的识别率。与基于Gabor的特征相比, 提出的方法具有更高的准确率和较低的特征维度。     

基于Gabor变换和Zernike矩的人脸特征提取方法

针对目前常用的三种人脸特征提取方法中存在的识别率低、抗噪性较弱的问题,提出一种基于Gabor变换和Zernike矩的人脸特征提取方法.该方法首先对人脸进行多分辨的Gabor变换,然后利用Zernike矩获得具有平移、尺度、旋转不变性的特征,并用线性判别分析(LDA)方法进一步进行特征选择,最后采用K最近邻分类方法进行人脸的识别.实验结果表明,在与常用的三种人脸特征提取方法的比较中,该方法具有更高的识别率和更强的抗噪性能.     

二维Gabor特征与三维NP-3DHOG特征融合的人脸识别算法

基于二维图像的人脸识别算法提取人脸纹理特征进行识别,但是光照、表情、人脸姿态等会对其产生不利影响。三维人脸特征能更精确地描述人脸的几何结构,并且不易受化妆和光照的影响,但只采用三维人脸数据进行人脸识别又缺少人脸纹理信息,因此文中将二维人脸特征与三维人脸特征相融合进行人脸识别。采用基于Gabor变换的二维特征与基于新的分块策略的三维梯度直方图特征相融合的算法进行人脸识别。首先,提取二维人脸的Gabor特征;然后,提取三维人脸基于新的分块策略的三维梯度直方图特征,旨在提取人脸的可辨别性特征;接下来,对二维人脸特征与三维人脸特征分别使用线性判别分析子空间算法进行训练,并使用加法原则融合两种特征的相似度矩阵;最后,输出识别结果。     

Hierarchical fusion of multi-spectral face images for improved recognition performance

This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.     

打卡图像的分层人脸比对识别方法研究

具有身份的人脸图像比对要求具有高的识别率和实时性。本文针对打卡人脸图像,提出了一种基于加权模板匹配和SVM的分层人脸识别方法。该方法利用Gabor小波变换进行人脸图像特征提取,采用贡献分析法分析特征的贡献权重,在待测人脸图像比对识别时,采用加权模板匹配进行比对,通过两个阈值的判断,在既不能认为比对正确和不正确的情形下,再采用SVM和库中人脸图像进行识别比对。实验结果表明,基于该方法的人脸比对识别率高、实时性好,可用于实时打卡人脸比对。     

Independent component analysis of Gabor features for face recognition

We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is twofold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FacE REcognition Technology (FERET) and the ORL datasets, where the images vary in illumination, expression, pose, and scale, show the feasibility of the IGF method. In particular, the IGF method achieves 98.5% correct face recognition accuracy when using 180 features for the FERET dataset, and 100% accuracy for the ORL dataset using 88 features.     

基于二维Gabor小波特征的三维人脸识别算法

分析三维人脸识别技术,提出一种基于Gabor小波特征的三维人脸识别算法。该算法采用二维Gabor小波特征精确且稳定地描述人脸特征,重建三维人脸模型并对其进行模板匹配,对匹配后的三维人脸模型进行线性判别分析。对基于ORL和UMIST两个人脸数据库的实验结果表明,该算法性能优良。     

改进随机子空间LDA结合多补丁集成学习的鲁棒人脸识别算法

针对化妆对人脸识别准确率的负面影响,提出了基于补丁集成学习的改进鲁棒人脸识别算法。首先,将每张人脸图像嵌入补丁中并用一组特征描述符描述每个补丁,即本地梯度Gabor模式(LGP)、Gabor空间定序定比测量直方图(HGSFRM)和密集采样局部多值模式(DSLMP )。然后,使用改进的随机子空间线性判别分析(SRS-LDA)方法采样补丁,并在化妆之前和化妆之后图像之间建立多个公共子空间进行集成学习。最后,利用协作和稀疏表示分类器比较这个子空间中的特征向量,同时通过求和规则联合得到的分数。实验将提出的算法在多种化妆数据集上进行评估分析,结果表明提出的算法相比于其他专为妆后人脸识别设计的算法有更高的识别精度。     

基于Gabor小波特征的多姿态人脸图像识别

多姿态人脸识别在很多领域具有重要的应用价值。基于多姿态人脸图像及Gabor小波特点选取离散化参数,对人脸图像进行Gabor小波变换;然后采用两步降维法对变换系数进行降维,基于降维后的Gabor特征表示实现人脸识别。实验将互不相交的两个样本集依次作为训练集和测试集,验证了该方法在人脸识别中对于不同姿态和表情的有效性及鲁棒性。     

结合Fisher判别的手写数字特征提取方法

在手写数字图像的特征提取中,提出一种结合Fisher线性判别的多分辨率Gabor滤波方法,在所有特征点上寻求特定滤波方向上的局部最优滤波频率,以获得最佳滤波效果,同时压缩不相关特征.在MNIST手写数字图像库上的识别实验表明:在小样本情况下,该方法能更准确地抽取手写数字图像特征,识别效果明显优于直接进行Gabor特征提取.     

联合Gabor误差字典和低秩表示的人脸识别算法

针对人脸图片的遮挡、伪装、光照及表情变化等问题,根据Gabor特征对遮挡、伪装、光照及表情变化有着更强的鲁棒性的特点,提出了联合Gabor误差字典和低秩表示的人脸识别算法(GDLRR)。首先对训练样本和测试样本分别进行Gabor特征提取,并将这些特征组成待测试的特征字典;然后将一个单位阵进行Gabor特征提取并训练成一个更紧凑的Gabor误差字典;最后联合Gabor误差字典和训练特征字典对测试特征字典进行低秩表示后进行分类识别。各类实验表明,提出的改进算法对人脸识别的各类问题都有着更强的鲁棒性和更高的识别准确率。     

基于主动表观模型姿态矫正和局部加权匹配人脸识别

非约束环境下,光照、姿态、表情、遮挡等复杂背景因素给人脸识别带来严重影响。提出一种基于AAM（active appearance model）的图像对齐和局部匹配人脸识别算法,使之能够增强人脸识别算法对姿态、表情变化的鲁棒性。AAM能够快速准确地定位人脸的特征点,进而将图像扭转到一个标准正面人脸模型中。接着,提出一种新的基于信息熵的Gabor jet加权方法用于提高人脸识别率;并且对Borda count分类器组合方法进行了改进,认为在投票过程中为其设置阈值来排除“噪声”的干扰可以提高识别率。通过与多种人脸识别方法的实验结果比较表明,使用AAM矫正图像后,联合熵加权Gabor方法和加阈值Borda能够取得比单独使用更好的成绩。     

基于能量的自适应局部Gabor特征提取的人脸识别

为了解决传统Gabor滤波器组在人脸识别过程中特征提取时间长、计算量大的问题,从不同方向、不同尺度以及全局角度按照能量大小构建了3种不同的局部Gabor滤波器组用来提取人脸特征。首先,分析数据库中部分图像Gabor变换后的图像能量,从不同角度选出能量较大的图像构建对应的局部Gabor滤波器组; 其次,根据所选滤波器组提取局部Gabor特征; 然后,采用线性判别分析(LDA)法进一步提取Fisher特征; 最后,利用最近邻法识别人脸图像。基于ORL人脸库和YALE人脸库的实验结果表明提出的人脸识别方法降低了人脸图像的特征维数,缩短了特征提取的时间,有效地提高了人脸识别率。     

Gabor特征融合的嵌入式人脸识别系统的研究

在嵌入式人脸识别系统中,由于多尺度Gabor抽取特征的维数和数据量过大,不适合在ARM板上实现,提出了多尺度Gabor特征加权融合的方法,很好地解决了图像维数和数据量过大的难点。加权融合过程包括多尺度Gabor特征的提取、特征权值的计算和加权融合过程。同时使用了类Haar特征提取人脸、2DPCA对人脸图像进行降维。基于EELiod 270嵌入式开发平台实现了一个嵌入式系统,结合典型图片库和实际图片进行了人脸识别测试,实践结果表明,系统在保证一定的识别率的同时,大幅度降低了运行时间,实时识别效果良好。