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.

Complete Discriminant Locality Preserving Projections for Face Recognition

To efficiently utilize the discriminant information in the range space of locality preserving total scatter, this paper proposes a complete discriminant locality preserving projections (CDLPP) algorithm for face recognition. Since Fisher discriminant analysis and locality preserving projections (LPP) have been widely used in face recognition, CDLPP algorithm integrates them together and analyzes the discriminant information contained in the principal spaces and null spaces of locality preserving within-class scatter, locality preserving between-class scatter and locality preserving total scatter. First, CDLPP algorithm removes the null space of locality preserving total scatter, in which no discriminant information is contained, using singular value decomposition (SVD). Then, regular discriminant features and irregular discriminant features of CDLPP are extracted severally in the null space and principal space of the locality preserving within-class scatter. Finally, both regular discriminant features and irregular discriminant features are concatenated to be used for face recognition. Extensive experiments on ORL face database, FERET subset and PIE subset illustrate that the performances of CDLPP outperform those of current subspace face recognition algorithms, such as LDA, LPP and discriminant LPP, which proves the effectiveness of the proposed algorithm.