基于深度自编码器的单样本人脸识别*

由于每个目标仅有一幅已知样本,无法描述目标的类内变化,诸多人脸识别算法在解决单样本人脸识别问题时识别性能较低.因此文中提出基于深度自编码器的单样本人脸识别算法.算法首先采用所有已知样本训练深度自编码器,得到广义深度自编码器,然后使用每个单样本目标的单个样本微调广义深度自编码器,得到特定类别的深度自编码器.识别时,将识别图像输入每个特定类别的深度自编码器,得到包含与测试图像相同类内变化的该类别的重构图像,使用重构图像训练Softmax回归模型,分类测试图像.在公共测试库上进行测试,并与其它算法在相同环境下进行对比,结果表明文中算法在获得更优识别率的同时,识别一幅图像所需平均时间更少.     

基于样本互作转换的人脸识别

针对人脸识别问题,因人脸图像具多姿态性、实际样本少等特性,且传统识别方法存在信息利用率低、分类过程繁琐等缺陷.为提高人脸识别精度并简化模型,提出了一种新的多人脸样本互作转换方法,将同时识别多个人脸的多分类问题简化为简单二分类问题,进行统一建模,充分利用图像信息,并基于可交换核函数消除互作样本中因初始样本排列顺序不同带来的影响,然后对产生的新特征进行非线性筛选,最后以简单投票策略对独立测试样本进行类别校正.基于ORL人脸数据库进行仿真,独立测试样本识别准确率高于95％,明显高于参比模型.实验结果表明,样本互作转换能有效简化识别模型,在海量数据识别中具有较好的应用价值.     

位置正则的支持向量域描述在人脸识别中的应用研究

支持向量域描述是一种有效的一分类数据描述方法,能够有效地对单一类别的数据进行表达,并能有效地降低负样本的干扰。应用支持向量域描述方法,将人脸图像集合投影到高维特征空间构建描述特征空间中人脸图像的超球体,并定义两个超球体之间的相似性度量,应用最近邻分类器进行分类。在基于集合的人脸识别应用标准数据库上测试了该方法,在Honda/UCSD、CMU Mobo和You Tube数据分别取得100%、97.55%和59.78%的识别率。实验结果表明,该方法是一种有效的基于图像集匹配的人脸识别方法。     

一种基于支持向量机的人脸识别新方法

关于人脸识别问题,采用一种基于独立分量分析进行特征提取和支持向量机实现多分类的人脸识别新方法.根据支持向量机理论,为提高对人脸的识别率,提出环形对称划分的支持向量机多分类算法.算法将多类问题的类别环形排列,依次进行对称划分构造纠错编码输出矩阵;根据求得的纠错编码输出矩阵,用解码函数求解待求样本的类别.对于人脸识别问题,利用独立分量分析方法构造人脸的特征脸空间,在特征脸空间运用算法进行人脸识别,在人脸数据库上的仿真结果表明,算法能有效地完成人脸识别任务.     

基于补集零空间与最近空间距离的人脸识别

针对人脸识别中在分类器判别时没有充分利用类间差异的问题,提出一种补集零空间（CNS）算法,并进一步提出结合CNS算法与最近空间距离的人脸识别算法——补集零空间与最近空间距离算法（CNSD）。首先,在训练样本中,对每一种类别的人脸样本,构建其子空间并计算其补集的零空间;其次,计算测试样本与所有子空间和补集零空间的距离,找到最小的子空间距离与最大的补集零空间距离对应的类别,将其判别为测试样本的类别。算法在ORL与AR人脸数据集上进行了测试,当训练样本数较小时,CNS算法与CNSD算法识别率远高于最近邻分类器（NN）算法、最近空间距离（NS）算法、最近最远空间距离（NFS）算法;训练样本数较大时,CNS算法与CNSD算法识别率也略高于NN算法、NS算法、NFS算法。实验结果表明,所提算法能充分利用图像的类间差异,提高人脸识别的成功率。     

分块双向2DPCA融合局部特征的人脸识别

针对(2D)2PCA无法保存某些重要局部特征的问题,提出一种分块双向二维主成分分析融合局部特征方法。首先,将图像分解为互不重叠的子块,每个子块包含重要的局部信息,利用(2D)2PCA对子块进行特征提取并投影到特征子空间。然后,对每个子块分别设计一个分类器并在一定置信度范围内判别测试样本所属类别。最后,根据所有子块所属类别的置信度之和完成人脸分类。在四个人脸识别数据库上的实验结果表明,相比其他几种人脸识别算法,该方法取得了更高的识别精度。     

全局和局部特征相融合的人脸识别算法

为了进一步提高特征提取效率和人脸识别正确率,提出一种融合全局和局部特征的人脸识别算法。引入局部散度矩阵和全局散度矩阵,两者分别表征样本的全局特征和局部特征;基于同类样本尽可能的紧密而异类样本尽可能远离的事实,构造最优化问题,采用支持向量机建立人脸分类器,并通过仿真实验测试算法的性能。仿真结果表明,该算法不仅提高了人脸识别正确率,而且提高了人脸识别效率。     

基于有监督保持邻域嵌入人脸识别

为了充分利用样本的类别信息,提高保持邻域嵌入算法在人脸识别中的识别性能,提出一种基于有监督保持邻域嵌入人脸识别方法(SNPE).按照线性鉴别的思想,通过可调因子把类间散布矩阵和类内散布矩阵融入到保持邻域嵌入算法的目标函数中,从而可以获得人脸样本的最有鉴别力的特征,最后用最近距离分类器分类.在AR和FERET人脸数据库上实验结果证明了该算法的有效性.     

一种新的有监督保局投影人脸识别算法

为了充分利用样本的类别信息,提出了一种新的有监督保局投影人脸识别算法（NSLPP）。首先,把类间散布矩阵融入到传统保局投影算法的目标函数中,修改目标函数,并基于新的目标函数得到变换矩阵;然后用线性鉴别的思想筛选出变换矩阵中的最优基向量,构成最终的变换矩阵,把训练样本和测试样本投影到有最优基向量构成的子空间得到训练样本和测试样本的特征;最后采用最近邻分类器分类,在ORL和FERET人脸库上的测试结果表明,NSLPP算法具有较好的识别性能。     

基于改进的有监督保局投影人脸识别算法

为了充分利用样本的类别信息,提出了一种改进的有监督保局投影人脸识别算法。利用先验类标签信息重新构造传统保局投影算法中的权重矩阵,基于改进后的保局投影算法得到变换矩阵;用线性鉴别的思想筛选出变换矩阵中的最优基向量,构成最终的变换矩阵。把训练样本和测试样本投影到由最优基向量构成的子空间得到训练样本和测试样本的特征。采用最近邻分类器分类。在ORL和FERET人脸库上的测试结果表明,算法具有较好的识别性能。     

Discriminative common vectors for face recognition

In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, we propose a new face recognition method called the discriminative common vector method based on a variation of Fisher's linear discriminant analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's linear discriminant criterion given in the paper. Our test results show that the discriminative common vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.     

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.     

求解广义最佳鉴别矢量集的一种改进算法

鉴于广义最佳临别矢量集是Ｆｏｌｅｙ－Ｓａｍｍｏｎ最佳鉴别矢量集的一种推广,给出了广义最佳鉴别矢量的定义,并从理论上对已有的求解广义最佳鉴别矢量集的算法作了分析,指出了其中的不足之处,并给出了一种改进的算法,将此方法用于人脸识别,结果显示,新方法比已有的方法更有效。     

An improved discriminative common vectors and support vector machine based face recognition approach

An improved discriminative common vectors and support vector machine based face recognition approach is proposed in this paper. The discriminative common vectors (DCV) algorithm is a recently addressed discriminant method, which shows better face recognition effects than some commonly used linear discriminant algorithms. The DCV is based on a variation of Fisher’s Linear Discriminant Analysis for the small sample size case. However, for multiclass problem, the Fisher criterion is clearly suboptimal. We design an improved discriminative common vector by adjustment for the Fisher criterion that can estimate the within-class and between-class scatter matrices more accurately for classification purposes. Then we employ support vector machine as the classifier due to its higher classification and higher generalization. Testing on two public large face database: ORL and AR database, the experimental results demonstrate that the proposed method is an effective face recognition approach, which outperforms several representative recognition methods.     

An adaptive feature fusion framework for multi-class classification based on SVM

An adaptive feature fusion framework is proposed for multi-class classification based on SVM. In a similar manner of one-versus-all (OVA), one of the multi-class SVM schemes, the proposed approach decomposes a multi-class classification into several binary classifications. The main difference lies in that each classifier is created with the most suitable feature vectors to discriminate one class from all the other classes. The feature vectors of the unknown samples are selected by each classifier adaptively such that recognition is fulfilled accordingly. In addition, novel evaluation criterions are defined to deal with the frequent small-number sample problems. A writer recognition experiment is carried out to accomplish this framework with three kinds of feature vectors: texture, structure and morphological features. Finally, the performance of the proposed approach is illustrated as compared with the OVA by applying the same feature vectors for all classes.     

人脸识别中基于核的子空间鉴别分析

尽管基于Fisher准则的线性鉴别分析被公认为特征抽取的有效方法之一，并被成功地用于人脸识别，但是由于光照变化、人脸表情和姿势变化，实际上的人脸图像分布是十分复杂的，因此，抽取非线性鉴别特征显得十分必要。为了能利用非线性鉴别特征进行人脸识别，提出了一种基于核的子空间鉴别分析方法。该方法首先利用核函数技术将原始样本隐式地映射到高维（甚至无穷维）特征空间；然后在高维特征空间里，利用再生核理论来建立基于广义Fisher准则的两个等价模型；最后利用正交补空间方法求得最优鉴别矢量来进行人脸识别。在ORL和NUST603两个人脸数据库上，对该方法进行了鉴别性能实验，得到了识别率分别为94％和99．58％的实验结果，这表明该方法与核组合方法的识别结果相当，且明显优于KPCA和Kernel fisherfaces方法的识别结果。     

最小类内方差支持向量引导的字典学习

支持向量引导的字典学习算法依据大间隔分类原则,仅考虑每类编码向量边界条件建立决策超平面,未利用数据的分布信息,在一定程度上限制了模型的泛化能力.为解决该问题,提出最小类内方差支持向量引导的字典学习算法.将融合Fisher线性鉴别分析和支持向量机大间隔分类准则的最小类内方差支持向量机作为鉴别条件,在模型分类器的交替优化过程中,充分考虑编码向量的分布信息,保障同类编码向量总体一致的同时降低向量间的耦合度并修正分类矢量,从而挖掘编码向量鉴别信息,使其更好地引导字典学习以提高算法分类性能.在人脸、物体和手写数字识别数据集上的实验结果表明,在大部分样本和原子数量条件下,该算法的识别率和原子鲁棒性均优于K奇异值分解、局部特征和类标嵌入约束等经典字典学习算法.     

A singular value decomposition representation based approach for robust face recognition

In the field of face recognition, sparse representation based classification (SRC) and collaborative representation based classification (CRC) have been widely used. Although both SRC and CRC have shown good classification results, it is still controversial whether it is sparse representation or collaborative representation that helps face recognition. In this paper, a new singular value decomposition based classification (SVDC) is proposed for face recognition. The proposed approach performs SVD on the training data of each class, and then determines the class of a test sample by comparing in which class of singular vectors it can be better represented. Experimental results on Yale B, PIE and UMIST datasets show that the proposed method achieves better recognition performance compared with several existing representation based classification algorithms. In addition, by adding Gaussian noise and Salt pepper noise to these datasets, it is proved that SVDC has better robustness. At the same time, the experimental results show that the recognition accuracy of the method acting on the training samples constructed by each class is higher than that of the method acting on the training sets constructed by all classes.

     

具有动态调节功能的Fisherface方法

高维、小样本数据的识别问题,始终是模式识别领域的热点和难点问题。由于训练样本数量很少,当以样本的协方差矩阵作为模式协方差矩阵的估计时,会产生较大的偏差。这是造成模式分类错误的主要原因。本文在详细论述Fisherface方法的基础上,提出了具有动态调节功能的Fisherface（DA-Fisherface）方法。该方法利用测试样本完成了对样本协方差矩阵的动态调节,减小了因样本数目很少所造成的偏差,从而实现了对Fisher鉴别矢量集的优化。最后,在ORL人脸库上的实验结果表明,该方法的模式分类正确率比Fisherface方法有显著提高。     

基于非凸低秩分解判别的叠加线性稀疏人脸识别

目的 针对因采集的人脸图像样本受到污染而严重干扰人脸识别及训练样本较少（小样本）时会由于错误的稀疏系数导致性能急剧下降从而影响人脸识别的问题,提出了一种基于判别性非凸低秩矩阵分解的叠加线性稀疏表示算法。方法 首先由γ范数取代传统核范数,克服了传统低秩矩阵分解方法求解核范数时因矩阵奇异值倍数缩放导致的识别误差问题;然后引入结构不相干判别项,以增加不同类低秩字典间的非相干性,达到抑制类内变化和去除类间相关性的目的;最后利用叠加线性稀疏表示方法完成分类。结果 所提算法在AR人脸库中的识别率达到了98.67±0.57%,高于SRC（sparse representation-based classification）、ESRC（extended SRC）、RPCA（robust principal component analysis）+SRC、LRSI（low rank matrix decomposition with structural incoherence）、SLRC（superposed linear representation based classification）-l₁等算法;同时,遮挡实验表明,算法对遮挡图像具有更好的鲁棒性,在不同遮挡比例下,相比其他算法均有更高的识别率。在CMU PIE人脸库中,对无遮挡图像添加0、10%、20%、30%、40%的椒盐噪声,算法识别率分别达到90.1%、85.5%、77.8%、65.3%和46.1%,均高于其他算法。结论 不同人脸库、不同比例遮挡和噪声的实验结果表明,所提算法针对人脸遮挡、表情和光照等噪声因素依然保持较高的识别率,鲁棒性更好。