Rapid and brief communication: Diagonal principal component analysis for face recognition

Authors:
Daoqiang Zhang;Zhi-Hua Zhou; Songcan Chen
Affiliations:
National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China and Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, ...;National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Venue:
Pattern Recognition
Year:
2006

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Abstract

In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. While in contrast to 2DPCA, DiaPCA reserves the correlations between variations of rows and those of columns of images. Experiments show that DiaPCA is much more accurate than both PCA and 2DPCA. Furthermore, it is shown that the accuracy can be further improved by combining DiaPCA with 2DPCA.