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2006

Diagonal principal component analysis for face recognition

8 years 10 months ago
Diagonal principal component analysis for face recognition
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.
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
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