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.     

Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition

This paper proposes a novel algorithm for image feature extraction, namely, the two-dimensional locality preserving projections (2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experimental results on the PolyU palmprint database show the effectiveness of the proposed algorithm.     

基于大间距准则的不相关保局投影分析

局部保持投影(Locality preserving projections,LPP)算法只保持了目标在投影后的邻域局部信息,为了更好地刻画数据的流形结构, 引入了类内和类间局部散度矩阵,给出了一种基于有效且稳定的大间距准则(Maximum margin criterion,MMC)的不相关保局投影分析方法.该方法在最大化散度矩阵迹差时,引入尺度因子α,对类内和类间局部散度矩阵进行加权,以便找到更适合分类的子空间并且可避免小样本问题; 更重要的是,大间距准则下提取的判别特征集一般情况下是统计相关的,造成了特征信息的冗余, 因此,通过增加一个不相关约束条件,利用推导出的公式提取不相关判别特征集, 这样做, 对正确识别更为有利.在Yale人脸库、PIE人脸库和MNIST手写数字库上的测试结果表明,本文方法有效且稳定, 与LPP、LDA (Linear discriminant analysis)和LPMIP(Locality-preserved maximum information projection)方法等相比,具有更高的正确识别率.     

基于局部不变映射的特征描述器算法

提出了一种新的基于局部不变映射(Locality preserving projections, LPP)的描述器设计算法. 该算法用LPP预先生成一个特征矩阵, 接着把特征点邻域内所有点的梯度组成一个高维的梯度向量, 然后通过特征矩阵把该梯度向量嵌入到一个低维的流形空间中, 生成一个维数很低的向量, 并把它作为该特征点的描述器. 所提出的算法能保持描述器之间的几何结构不变: 原空间中邻接的描述器映射到低维空间后保持邻接, 而不相似的描述器映射后区分度更大, 所以该算法所生成的描述器能表现特征点之间的内在关系, 具有很强的鲁棒性. 通过与SIFT (Scale invariant feature transform), PCA-SIFT的实验比较, 此算法更快速, 更具鲁棒性.     

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.     

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。     

Comment on “Facial expression recognition based on two-dimensional discriminant locality preserving projections” [Neurocomputing 71 (2008) 1730-1734]

In this note, we show that the mathematical formulation of the two-dimensional discriminant locality preserving projections (2D-DLPP) proposed in the paper [R. Zhi, Q. Ruan, Facial expression recognition based on two-dimensional discriminant locality preserving projections, Neurocomputing 71 (2008) 1730–1734] is not very sound. The rigorous version is thus given. We also point out that 2D-DLPP can be viewed from the perspective of the discriminant locality preserving projections (DLPP).     

Facial expression recognition based on two-dimensional discriminant locality preserving projections

In this paper, a novel method called two-dimensional discriminant locality preserving projections (2D-DLPP) is proposed. By introducing between-class scatter constraint and label information into two-dimensional locality preserving projections (2D-LPP) algorithm, 2D-DLPP successfully finds the subspace which can best discriminate different pattern classes. So the subspace obtained by 2D-DLPP has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. The proposed method was applied to facial expression recognition tasks on JAFFE and Cohn-Kanade database and compared with other three widely used two-dimensional methods: 2D-PCA, 2D-LDA and 2D-LPP. The high recognition rates show the effectiveness of the proposed algorithm.     

Exponential locality preserving projections for small sample size problem

Locality preserving projections (LPP) is a widely used manifold reduced dimensionality technique. However, it suffers from two problems: (1) small sample size problem and (2) the performance is sensitive to the neighborhood size k. In order to address these problems, we propose an exponential locality preserving projections (ELPP) by introducing the matrix exponential in this paper. ELPP avoids the singular of the matrices and obtains more valuable information for LPP. The experiments are conducted on three public face databases, ORL, Yale and Georgia Tech. The results show that the performances of ELPP is better than those of LPP and the state-of-the-art LPP Improved1.     

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。     

基于局部保持映射的图像隐密检测算法

提出了一种新的基于局部保持映射（Locality Preserving Projections,LPP）降维的图像隐密检测方案。为降低图像特征向量的维数,同时保持其内在低维结构,方便构造更有效的分类器,在经过小波变换形成图像特征后,利用LPP算法得到图像特征集的低维流形,实现对图像高维特征的降维。进而使用支持向量机（SVM）算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类。实验结果表明,与不采用降维算法的检测方案相比,提出的方案能够显著地提高检测的准确率。     

A novel process monitoring and fault detection approach based on statistics locality preserving projections

Data-driven fault detection technique has exhibited its wide applications in industrial process monitoring. However, how to extract the local and non-Gaussian features effectively is still an open problem. In this paper, statistics locality preserving projections (SLPP) is proposed to extract the local and non-Gaussian features. Firstly, statistics pattern analysis (SPA) is applied to construct process statistics and grasp the non-Gaussian statistical property using high order statistics. Then, locality preserving projections (LPP) method is used to discover local manifold structure of the statistics. In essence, LPP tries to map the close points in the original space to close in the low-dimensional space. Lastly, T² and squared prediction error (SPE) charts of SLPP model are used to detect process faults. One simple simulated system and the Tennessee Eastman process show that the proposed SLPP method is more effective than principal component analysis, LPP and statistics principal component analysis in fault detection performance.     

保局投影在图像隐密检测中的应用

为解决图像隐密检测中图像特征维数过高导致的"维数灾难"问题,在保持图像特征内在低维结构的基础上降低特征向量的维数,方便构造更有效的分类器,提出了一种基于保局投影(locality preserving projections,LPP)降维的图像隐密检测算法,对待测图像进行小波变换形成图像特征后,利用LPP算法实现对图像高维特征的降维,得到图像特征集的低维流形.使用支持向量机(SVM)算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类.实验结果表明,与不使用降维算法的检测方案相比,基于LPP降维的检测算法能够显著地提高检测的准确率.     

基于无参数二维判别局部保持投影算法的人脸识别

通过向二维局部保持投影（2D-LPP）算法中引入类间约束和类标识信息,得到二维判别局部保持投影（2D-DLPP）算法,使它拥有更多的判别信息。但它却面临复杂的参数选择问题,这使得它在解决识别问题时受到限制。为解决此问题,构造无参数的相似矩阵,提出无参数的二维判别局部投影（无参数2D-DLPP）算法。在Yale和ORL人脸库上的仿真实验结果表明,该算法与二维判别局部保持投影（2D-DLPP）、二维局部保持投影法（2D-LPP）和二维线性判别分析法（2D-LDA）相比能够取得更高的识别率。     

基于L1范数的分块二维局部保持投影算法

针对高维输入数据维数较大时可能存在奇异值问题,同时为提高算法的运算效率以及算法的鲁棒性,提出了一种基于L1范数的分块二维局部保持投影算法B2DLPP-L1。传统的局部保持投影算法为避免出现奇异值问题,首先运用主成分分析算法将高维数据投影到子空间中,然而这种方式将会造成高维数据中部分有效信息的流失,B2DLPP-L1算法选择将二维数据直接作为输入数据,避免运用向量形式的输入数据时可能造成的数据流失;同时该算法对二维输入数据进行分块处理,将分块后的数据块作为新的输入数据,之后运用基于L1范数的二维局部保持投影算法对其进行降维。理论上,B2DLPP-L1算法能够较好地对数据进行降维,不仅能够保持高维数据中的有效信息,降低计算复杂程度,提高算法的运行效率,同时还能够克服存在外点情况下分类准确率较低问题,提高算法的鲁棒性。通过选择不同的人脸数据库进行实验,实验结果表明,在存在外点的情况下,运用最近邻分类器时能够取得更高的分类准确率,同时所需的分类时间有所减少。     

On minimum class locality preserving variance support vector machine

In this paper, a so-called minimum class locality preserving variance support machine (MCLPV_SVM) algorithm is presented by introducing the basic idea of the locality preserving projections (LPP), which can be seen as a modified class of support machine (SVM) and/or minimum class variance support machine (MCVSVM). MCLPV_SVM, in contrast to SVM and MCVSVM, takes the intrinsic manifold structure of the data space into full consideration and inherits the characteristics of SVM and MCVSVM. We discuss in the paper the linear case, the small sample size case and the nonlinear case of the MCLPV_SVM. Similar to MCVSVM, the MCLPV_SVM optimization problem in the small sample size case is solved by using dimensionality reduction through principal component analysis (PCA) and one in the nonlinear case is transformed into an equivalent linear MCLPV_SVM problem under kernel PCA (KPCA). Experimental results on real datasets indicate the effectiveness of the MCLPV_SVM by comparing it with SVM and MCVSVM.     

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。     

LPMVP算法及其在故障检测中的应用

针对数据信息的特征提取和降维问题, 提出一种局部保持最大方差投影 (Locality preserving maximum varianceprojections, LPMVP) 新算法. 该算法综合考虑了主元分析(Principalcomponent analysis, PCA)和局部保持投影(Locality preservingprojections, LPP)算法的优点和不足, 提出了新的优化目标, 使投影得到的低维空间不仅和原始变量空间有相似的局部近邻结构, 而且有相似的整体结构, 因而可以包含更多的特征信息. 在此基础上, 本文使用LPMVP算法把原始变量空间划分为特征空间和残差空间, 分别构造了T2和SPE统计量对过程进行监测, 建立了一种新的故障检测方法. 通过数值例子以及TE过程的仿真研究, 表明了LPMVP算法可以有效地提取数据信息, 同时也体现了较强的故障检测能力.     

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。     

Regularized locality preserving discriminant analysis for face recognition

This paper proposes a regularized locality preserving discriminant analysis (RLPDA) approach for facial feature extraction and recognition. The RLPDA approach decomposes the eigenspace of the locality preserving within-class scatter matrix into three subspaces, i.e., the face space, the noise space and the null space, and then regularizes the three subspaces differently according to their predicted eigenvalues. As a result, the proposed approach integrates discriminative information in all of the three subspaces, de-emphasizes the effect of the eigenvectors corresponding to the small eigenvalues, and meanwhile suppresses the small sample size problem. Extensive experiments on ORL face database, FERET face subset and UMIST face database illustrate the effectiveness of the proposed approach.