有监督的无参数核局部保持投影及人脸识别

针对发掘人脸图像中的高维非线性结构,将加核及构造无参数近邻图两种思想同时引入到局部保持投影算法中,在有监督的模式下,提出了一种新的有监督的无参数核局部保持投影(Parameter-less Supervised Kernel Locality Preserving Projection,PSKLPP)算法并给出了其推导过程。该算法通过将欧氏距离改为对离群数据更为鲁棒的余弦距离,构造无参数近邻图,利用核方法提取人脸图像中的非线性信息,并将其投影在一个高维非线性空间,运用局部保持投影算法得到一线性映射,有效避免了在计算相似矩阵过程中面临的复杂参数选择问题。在ORL和Yale人脸库上的仿真实验验证了所提算法的有效性。     

Locality Regularization Embedding for face verification

Graph embedding (GE) is a unified framework for dimensionality reduction techniques. GE attempts to maximally preserve data locality after embedding for face representation and classification. However, estimation of true data locality could be severely biased due to limited number of training samples, which trigger overfitting problem. In this paper, a graph embedding regularization technique is proposed to remedy this problem. The regularization model, dubbed as Locality Regularization Embedding (LRE), adopts local Laplacian matrix to restore true data locality. Based on LRE model, three dimensionality reduction techniques are proposed. Experimental results on five public benchmark face datasets such as CMU PIE, FERET, ORL, Yale and FRGC, along with Nemenyi Post-hoc statistical of significant test attest the promising performance of the proposed techniques.     

无关性判别保局算法及其在人脸识别中的应用

特征提取是人脸识别过程中的一个重要步骤,是人脸识别算法有效性的关键。提出了一种基于无关性判别保局的特征提取算法,并应用于人脸识别。基于保局投影算法的人 脸识别是一种有效的人脸识别算法,但它只考虑了数据的局部性,没有考虑类别信息,也没有考虑所提特征之间的相关性,现有的改进算法虽然考虑了类别信息,但是没有考虑到 类间信息。本文算法使得所提特征之间相互无关,这样降低了数据冗余,同时考虑到类别信息,使得投影后的类间区分度加强了。实验结果验证了算法的正确性和有效性,比传统 算法有较好的识别性能。     

Locality preserving and global discriminant projection with prior information

Existing supervised and semi-supervised dimensionality reduction methods utilize training data only with class labels being associated to the data samples for classification. In this paper, we present a new algorithm called locality preserving and global discriminant projection with prior information (LPGDP) for dimensionality reduction and classification, by considering both the manifold structure and the prior information, where the prior information includes not only the class label but also the misclassification of marginal samples. In the LPGDP algorithm, the overlap among the class-specific manifolds is discriminated by a global class graph, and a locality preserving criterion is employed to obtain the projections that best preserve the within-class local structures. The feasibility of the LPGDP algorithm has been evaluated in face recognition, object categorization and handwritten Chinese character recognition experiments. Experiment results show the superior performance of data modeling and classification to other techniques, such as linear discriminant analysis, locality preserving projection, discriminant locality preserving projection and marginal Fisher analysis.     

基于有监督直接局部保持投影的人脸识别

提出一种用于图像识别的有监督直接局部保持投影算法,该算法结合样本类别信息,通过同时对角化的方法求解局部保持投影问题,避免矩阵的奇异性。在ORL人脸库上的测试结果表明,该算法的识别率高于PCA, PCA+LPP等方法。     

Kernel class-wise locality preserving projection

In the recent years, the pattern recognition community paid more attention to a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel local structure based feature extraction method, called class-wise locality preserving projection (CLPP). CLPP utilizes class information to guide the procedure of feature extraction. In CLPP, 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 the class information. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Moreover, a kernel version of CLPP namely Kernel CLPP (KCLPP) is developed through applying the kernel trick to CLPP to increase its performance on nonlinear feature extraction. Experiments on ORL face database and YALE face database are performed to test and evaluate the proposed algorithm.     

Maximal local interclass embedding with application to face recognition

Dimensionality reduction of high dimensional data is involved in many problems in information processing. A new dimensionality reduction approach called maximal local interclass embedding (MLIE) is developed in this paper. MLIE can be viewed as a linear approach of a multimanifolds-based learning framework, in which the information of neighborhood is integrated with the local interclass relationships. In MLIE, the local interclass graph and the intrinsic graph are constructed to find a set of projections that maximize the local interclass scatter and the local intraclass compactness simultaneously. This characteristic makes MLIE more powerful than marginal Fisher analysis (MFA). MLIE maintains all the advantages of MFA. Moreover, the computational complexity of MLIE is less than that of MFA. The proposed algorithm is applied to face recognition. Experiments have been performed on the Yale, AR and ORL face image databases. The experimental results show that owing to the locally discriminating property, MLIE consistently outperforms up-to-date MFA, Smooth MFA, neighborhood preserving embedding and locality preserving projection in face recognition.     

局部非参数子空间分析在人脸识别中的应用

提出了一种局部非参数子空间分析算法（Local Nonparametric Subspace Analysis,LNSA）,将其应用在人脸识别中。LNSA算法结合了非参数子空间算法（Nonparametric Subspace Analysis,NSA）与局部保留投影算法（Locality Preserving Projection,LPP）。它利用LPP算法中的相似度矩阵重构NSA的类内散度矩阵,使得在最大化类间散度矩阵的同时保留了类的局部结构。在ORL人脸库和XM2VTS人脸库上作了实验并证明LNSA方法要优于其他方法。     

一种新的有监督的局部保持典型相关分析算法

从模式识别的角度出发,在局部保持典型相关分析的基础上,提出一种有监督的局部保持典型相关分析算法(SALPCCA)。该方法在构造样本近邻图时将样本的类别信息考虑在内,由样本间的距离度量确定权重,建立样本间的多重权重相关,通过使同类内的成对样本及其近邻间的权重相关性最大,从而能够在利用样本的类别信息的同时,也能保持数据的局部结构信息。此外,为了能够更好地提取样本的非线性信息,将特征集映射到核特征空间,又提出一种核化的SALPCCA(KSALPCCA)算法。在ORL、Yale、AR等人脸数据库上的实验结果表明,该方法较其他的传统典型相关分析方法有着更好的识别效果。     

一种用于特征提取的保局判别分析算法

针对保局投影(LPP)为无监督算法的局限,提出了一种新的监督版的LPP,即保局判别分析(LPDA)算法。LPDA吸收了流形学习算法与最大边界准则(MMC)的共同特点,可以将高维的人脸数据投影到低维子空间,具有能处理新样本与无小样本问题的优点。与现有的多种经典相关方法相比,从Yale, UMIST及MIT 3个人脸数据库的实验结果表明,提出的LPDA算法在降维的同时提取了用于人脸识别的更有效的特征,人脸图像识别性能较好,具有较强的判别分析能力。     

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。     

完备鉴别保局投影人脸识别算法

为了充分利用保局总体散布主元空间内的鉴别信息进行人脸识别,提出了一种完备鉴别保局投影(complete discriminant locality preserving projections,简称CDLPP)人脸识别算法.鉴于Fisher鉴别分析和保局投影已经被广泛的应用于人脸识别,完备鉴别保局投影(locality preserving projections,简称LPP)算法将这两者结合起来,分析了保局类内散布、类间散布和总体散布的主元空间和零空间内包含的鉴别信息.该算法采用奇异值分解(singular value decomposition,简称SVD),去除了不含任何鉴别信息的保局总体散布的零空间;分别在保局类内散布的主元空间和零空间提取规则鉴别特征和不规则鉴别特征;用串联的方式在特征层融合规则鉴别特征和不规则鉴别特征形成完备的鉴别特征进行人脸识别.在ORL库、FERET子库和PIE子库上的大量识别实验充分表明了完备鉴别保局投影算法的性能优于线性鉴别分析、保局投影和鉴别保局投影等现有的子空间人脸识别算法,验证了算法的有 效性.     

子模式局部保持映射人脸识别

研究表明基于整体思想的人脸识别方法由于忽略图像的局部信息,在识别性能方面不如局部信息特征保持较好的基于子模块思想的识别算法。基于应用流形技术对图像降维后能够较好保持非线性子流形中的局部数据流形结构,提出了一种改进的子模式局部保持映射人脸识别算法。其主要思想是将同类的不同图像一并划分子集,由同位置子图组成子模块,并对子模块运用LPP算法学习其流形结构,与将不同类图像一并划分子集学习流形的方法不同。实验表明,该算法能更好地保持人脸图像的局部流形结构和信息特征,提高了识别率。     

基于SLPP和张量分解相结合的人脸识别*

针对多线性分析算法对多姿态多身份因素并存时,人脸的识别率大大下降等问题,提出了带监督的局 部保留投影映射算法与多线性张量分析算法相结合的人脸识别方法。该方法将人脸转动的近邻点信息作为监 督信息引入,更精确地描述了姿态空间的非线性结构,再结合张量分解和核函数将姿态流形系数映射到高维图 像空间,使得从低维空间到高维空间映射的精确性得以提高。在东方人脸数据库上进行实验,结果验证了该算 法的有效性。     

基于判别正则化的局部保留投影

局部保留投影（Locality preserving projections,LPP）是一种常用的线性化流形学习方法,其通过线性嵌入来保留基于图所描述的流形数据本质结构特征,因此LPP对图的依赖性强,且在嵌入过程中缺少对图描述的进一步分析和挖掘。当图对数据本质结构特征描述不恰当时,LPP在嵌入过程中不易实现流形数据本质结构的有效提取。为了解决这个问题,本文在给定流形数据图描述的条件下,通过引入局部相似度阈值进行局部判别分析,并据此建立判别正则化局部保留投影（简称DRLPP）。该方法能够在现有图描述的条件下,有效突出不同流形结构在线性嵌入空间中的可分性。在人造合成数据集和实际标准数据集上对DRLPP以及相关算法进行对比实验,实验结果证明了DRLPP的有效性。     

基于L1范数的二维局部保留映射

提出了一种基于L1范数的二维局部保留映射(two-dimensional locality preserving projections based on L1-norm,2DLPP-L1)特征提取方法。与传统的基于L2范数的二维局部保留映射(2DLPP)相比,所提方法有两个优点。首先,由于L1范数对噪声不敏感,因此它具有更强的抗噪声能力;其次,它不需要进行特征值分解。在两个人脸数据库和一个手写数字数据集上的实验结果表明,当训练集中有噪声时,所提的2DLPP-L1能够取得优于传统2DLPP的分类性能。     

面向酉子空间的二维判别保局投影的人脸识别*

保局投影算法（LPP）在人脸识别中具有较好的识别性能,但它是一种非监督学习,并且在具体实现时需要把图像转换为向量,破坏了图像的像素结构,这显然不利于模式识别。针对这些问题,提出基于酉子空间的二维判别保局算法,不仅在判别保局算法的基础上增加了类别信息,而且直接在灰度矩阵上进行水平和垂直方向上的二维保局投影。该方法构造酉空间上的复向量后再运用线性判别分析提取特征。在ORL、Yale和XJTU人脸库中验证了算法的正确性和有效性,其识别率比传统的2DLDA和2DLPP等方法提高4～5个百分点。     

Sparse two-dimensional local discriminant projections for feature extraction

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) and two-dimensional discriminant locality preserving projections (2DDLPP) were recently proposed to directly extract features form 2D face matrices to improve the performance of two-dimensional locality preserving projections (2DLPP). But all of them require a high computational cost and the learned transform matrices lack intuitive and semantic interpretations. In this paper, we propose a novel method called sparse two-dimensional locality discriminant projections (S2DLDP), which is a sparse extension of graph-based image feature extraction method. S2DLDP combines the spectral analysis and L₁-norm regression using the Elastic Net to learn the sparse projections. Differing from the existing 2D methods such as 2DLPP, 2DDLP and 2DLGEDA, S2DLDP can learn the sparse 2D face profile subspaces (also called sparsefaces), which give an intuitive, semantic and interpretable feature subspace for face representation. We point out that using S2DLDP for face feature extraction is, in essence, to project the 2D face images on the semantic face profile subspaces, on which face recognition is also performed. Experiments on Yale, ORL and AR face databases show the efficiency and effectiveness of S2DLDP.     

有监督正交局部保留投影及其在人脸识别中的应用

针对局部保留投影算法（LPP）的无监督和非正交问题,提出了一种有监督的正交局部保留投影算法SOLPP。该算法同时考虑了样本的类别信息以及投影向量间的相互正交性,首先利用样本的类标签信息重新定义了类内和类间相似度矩阵,同时最大化类间离散度与类内离散度之比,有效地保持了样本的局部结构;其次对投影基向量进行正交化,在保持数据空间结构的同时进一步提高了人脸识别效果。在ORL和FERET人脸库上的实验表明,该方法的识别率要优于SLPP等算法。     

Face recognition using discriminative locality preserving vectors

We proposed an effective face recognition method based on the discriminative locality preserving vectors method (DLPV). Using the analysis of eigenspectrum modeling of locality preserving projections, we selected the reliable face variation subspace of LPP to construct the locality preserving vectors to characterize the data set. The discriminative locality preserving vectors (DLPV) method is based on the discriminant analysis on the locality preserving vectors. Furthermore, the theoretical analysis showed that the DLPV is viewed as a generalized discriminative common vector, null space linear discriminant analysis and null space discriminant locality preserving projections, which gave the intuitive motivation of our method. Extensive experimental results obtained on four well-known face databases (ORL, Yale, Extended Yale B and CMU PIE) demonstrated the effectiveness of the proposed DLPV method.