基于局部中频Gabor滤波器组的人脸识别

针对传统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,提出一种利用局部Gabor滤波器组进行人脸图像中频特征提取的方法.选择中频带的Gabor滤波器构造局部中频Gabor滤波器组;提取局部Gabor中频特征;采用线性判别分析法(linear discriminate analysis,LDA)进一步提取Fisher特征,得到图像的Gabor+ Fisher特征,利用最近邻法进行人脸图像识别.基于ORL和AR人脸库的实验结果表明,基于此局部Gabor滤波器组的人脸识别方法较传统的Gabor特征提取方法降低了40％的特征维数,加快了特征提取速度,提高了人脸识别率.     

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。     

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。     

基于Gabor滤波和KPCA的人脸识别方法

尽管核主分量分析能够有效地提取非线性特征,并成功地应用于人脸识别,但是抽取对光照、表情不敏感的特征仍然是亟待解决的问题。该文提出了一种结合Gabor特征和核主分量分析的人脸识别方法。首先通过Gabor滤波器对人脸图像滤波,并通过实验分析了Gabor滤波器参数的选择,然后采用核主分量分析的方法降低Gabor特征的维数．最后采用最近邻分类器进行识别。由于采用了Gabor滤波,该方法对光照、表情具有鲁棒性,在ORL人脸库上的实验结果表强,该方法在识别性能上优于核主分量分析方法。     

基于Gabor滤波和KPCA的人脸识别方法

尽管核主分量分析能够有效地提取非线性特征,并成功地应用于人脸识别,但是抽取对光照、表情不敏感的特征仍然是亟待解决的问题.该文提出了一种结合Gabor特征和核主分量分析的人脸识别方法.首先通过Gabor滤波器对人脸图像滤波,并通过实验分析了Gabor滤波器参数的选择,然后采用核主分量分析的方法降低Gabor特征的维数,最后采用最近邻分类器进行识别.由于采用了Gabor滤波,该方法对光照、表情具有鲁棒性,在ORL人脸库上的实验结果表明,该方法在识别性能上优于核主分量分析方法.     

基于彩色Gabor特征的人脸识别技术

提出了一种利用所提取的彩色Gabor特征来提高人脸识别系统性能的方法。首先利用四元数表示彩色信息,考虑到Gabor滤波器具有空间局部性和方向选择性的特点,将其扩展到四元数空间。然后通过人脸图像特征点与Gabor滤波器的卷积来提取特征,这样就将传统的灰度Gabor特征拓展为彩色Gabor特征。最后对于所提取的特征利用PCA降维后送入支持向量机中分类。实验采用彩色FERET人脸库并利用ROC曲线进行交叉检验,结果说明通过提取和利用这种彩色纹理信息能显著提高人脸识别系统性能。     

动态视频人脸考勤方法研究与实现

研究并实现了利用Gabor滤波器和Fisher线性鉴别分析(FLDA)方法的动态人脸识别考勤系统.系统实现的基本思想是运用Gabor变换提取人脸的局部特征和经过Gabor处理后使得人脸对光照变化不敏感;进一步利用FLDA来降维和隐含地提取最有利于分类的最佳鉴别特征;最后将视频采集的考勤图像与训练库中的图像通过比对,得出识别结果.实验结果表明,利用该方法开发的动态人脸识别考勤系统具有识别率高、实用性好、可靠性强等特点.     

结合Gabor特征和自适应加权Fisher准则的人脸识别

在基于Fisher准则的字典学习算法中,初始字典的选取和目标函数的构建,严重影响字典学习的效果。为了减少初始字典的影响,提高算法的表达和判别能力。提出了一种结合Gabor特征和自适应加权Fisher准则的人脸识别算法。该算法首先采用Gabor滤波器提取人脸特征,将提取到的Gabor人脸特征作为人脸训练集;通过添加遗忘函数和根据样本间的距离对训练样本自适应加权,改进Fisher准则字典学习算法;利用测试样本编码系数的误差进行识别。在人脸库上的实验表明,算法不仅能很好地提取图像的特征信息,而且可以有效地提高人脸识别率。     

基于深度自动编码器的机场安检人脸识别系统设计

由于现有的机场安检系统通常只针对行李和旅客携带的违禁物品进行检测,没有考虑到对于正在通缉的罪犯进行监控和核查,设计了一种基于Gabor小波滤波和深度自动编码器的机场安检人脸识别系统;首先,以ATmega128L为处理器核心,SA7111作为模数转换器,MAX7000作为全局逻辑控制单元设计了机场人脸识别系统硬件,然后采用Gabor小波函数作为卷积核函数,在对原始图像分块的基础上进行卷积,采用多个RBM堆叠组成的自动编码器,通过比较差异算法训练RBM从而自动提取人脸特征,最后构建一个三层的BP神经网络,将自动提取的人脸特征作为输入,将图像标签作为输出层,并通过反向传播算法训练网络进行人脸识别;通过部署仿真实验环境对文中方法进行验证,仿真结果表明:文中系统能较为精确地实现人脸识别,与其它方法相比,具有识别率高和收敛速度快的优点.     

Gabor 滤波器参数设置

利用Gabor滤波器进行特征提取时,不同的Gabor滤波器参数所提取的特征具有不同的特点,首先从理论上分析了Gabor滤波器不同的时域（频域）窗口尺寸、不同的Gabor滤波器方向对所提取特征的影响;然后分析不同Gabor滤波器模板尺寸对所提取特征的影响;最后利用CAS-PEAL-R1人脸库进行仿真实验.     

Kernel-based improved discriminant analysis and its application to face recognition

Kernel discriminant analysis (KDA) is a widely used tool in feature extraction community. However, for high-dimensional multi-class tasks such as face recognition, traditional KDA algorithms have the limitation that the Fisher criterion is nonoptimal with respect to classification rate. Moreover, they suffer from the small sample size problem. This paper presents a variant of KDA called kernel-based improved discriminant analysis (KIDA), which can effectively deal with the above two problems. In the proposed framework, origin samples are projected firstly into a feature space by an implicit nonlinear mapping. After reconstructing between-class scatter matrix in the feature space by weighted schemes, the kernel method is used to obtain a modified Fisher criterion directly related to classification error. Finally, simultaneous diagonalization technique is employed to find lower-dimensional nonlinear features with significant discriminant power. Experiments on face recognition task show that the proposed method is superior to the traditional KDA and LDA.     

Face recognition using a kernel fractional-step discriminant analysis algorithm

Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kernel fractional-step discriminant analysis (KFDA) for nonlinear feature extraction and dimensionality reduction. Not only can this new algorithm, like other kernel methods, deal with nonlinearity required for many face recognition tasks, it can also outperform traditional KDA algorithms in resisting the adverse effects due to outlier classes. Moreover, to further strengthen the overall performance of KDA algorithms for face recognition, we propose two new kernel functions: cosine fractional-power polynomial kernel and non-normal Gaussian RBF kernel. We perform extensive comparative studies based on the YaleB and FERET face databases. Experimental results show that our KFDA algorithm outperforms traditional kernel principal component analysis (KPCA) and KDA algorithms. Moreover, further improvement can be obtained when the two new kernel functions are used.     

基于SVD压缩降秩与KDA的人脸识别新方法

文中提出了一种基于奇异值压缩降秩与核判别分析（KDA）变换方法的人脸特征提取新方法,同时结合对偶传播人工神经网络（CPN）对不同的人脸图像进行识别分类。该方法首先采用奇异值分解压缩降秩准则对人脸图像进行择优奇异值的选取,然后对提取后的择优特征值进行核判别分析（KDA）变换,进一步提取人脸图像最优特征值,最后将得到的人脸图像最优特征值作为网络的输入值,利用对偶传播人工神经网络（CPN）对人脸图像进行识别分类。实验结果表明该方法具有较高的识别率和较快的识别速度。     

基于增强Gabor特征和直接分步线性判别分析的人脸识别

Gabor特征能从不同方向和尺度有效表示人脸图片的局部特征,但是利用传统Gabor特征的方法却忽略原始人脸图片所包含的全局特征。文中把Gabor特征和原始图片信息结合起来,构成增强的Gabor特征,并结合直接分步线性判别分析算法,提出一种人脸识别方法。在Yale、ORL和Georgia Tech人脸库的仿真实验结果表明,相对于传统Gabor特征,增强Gabor特征能够有效提高人脸识别率。     

Bilinear analysis for kernel selection and nonlinear feature extraction.

This paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases.     

Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models

The selection of kernel function and its parameter influences the performance of kernel learning machine. The difference geometry structure of the empirical feature space is achieved under the different kernel and its parameters. The traditional changing only the kernel parameters method will not change the data distribution in the empirical feature space, which is not feasible to improve the performance of kernel learning. This paper applies kernel optimization to enhance the performance of kernel discriminant analysis and proposes a so-called Kernel Optimization-based Discriminant Analysis (KODA) for face recognition. The procedure of KODA consisted of two steps: optimizing kernel and projecting. KODA automatically adjusts the parameters of kernel according to the input samples and performance on feature extraction is improved for face recognition. Simulations on Yale and ORL face databases are demonstrated the feasibility of enhancing KDA with kernel optimization.     

基于核典型相关分析的多特征组合人脸识别

为了提高人脸的识别率,利用多特征和分类器之间的互补优势,提出一种基于核典型相关分析的多特征组合人脸识别方法（KCCA-MF）。提取人脸图像的LBP特征和Gabor特征,采用核典型相关分析算法对两种特征进行融合,以消除冗余特征,采用K近邻算法和支持向量机建立组合人脸分类器,并采用3个经典人脸库进行仿真分析。结果表明,相对于其他人脸识别方法,KCCA-MF提高了人脸识别的识别准确率和效率,可以满足人脸识别的实时性要求。     

Speed up kernel discriminant analysis

Linear discriminant analysis (LDA) has been a popular method for dimensionality reduction, which preserves class separability. The projection vectors are commonly obtained by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. LDA can be performed either in the original input space or in the reproducing kernel Hilbert space (RKHS) into which data points are mapped, which leads to kernel discriminant analysis (KDA). When the data are highly nonlinear distributed, KDA can achieve better performance than LDA. However, computing the projective functions in KDA involves eigen-decomposition of kernel matrix, which is very expensive when a large number of training samples exist. In this paper, we present a new algorithm for kernel discriminant analysis, called Spectral Regression Kernel Discriminant Analysis (SRKDA). By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework, which facilitates both efficient computation and the use of regularization techniques. Specifically, SRKDA only needs to solve a set of regularized regression problems, and there is no eigenvector computation involved, which is a huge save of computational cost. The new formulation makes it very easy to develop incremental version of the algorithm, which can fully utilize the computational results of the existing training samples. Moreover, it is easy to produce sparse projections (Sparse KDA) with a L ₁-norm regularizer. Extensive experiments on spoken letter, handwritten digit image and face image data demonstrate the effectiveness and efficiency of the proposed algorithm.     

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.     

一种非参数核函数鉴别分析法及其在人脸识别中的应用

核判别分析(KDA)算法仅考虑c-1个判别特征,且计算类间离散度矩阵时需使用所有的训练样本,而一些有利于分类的边界结构未能被提取。为此,提出了一种非参数非线性(核)鉴别分析方法,其在计算特征空间中的类间散布矩阵时引入一个权值函数,从而能提取有利于分类的边界结构。仿真试验表明,新方法在识别性能上优于已有的一些方法,且避免了使用繁琐的矩阵奇异值分解理论,有一定的实用价值。