Abstract To solve the problems in feature extraction algorithms, an algorithm based on linear discriminant analysis (LDA), called classification probability preserving discriminant analysis (CPPDA), is proposed for face recognition. Firstly, the classification probability of each sample is computed by CPPDA, and both the between-class scatter matrix and the within-class scatter matrix are redefined by the classification probability. Secondly, through maximizing the between-class scatter and minimizing the within-class scatter simultaneously, an optimal projection matrix can be preserved in the low-dimensional feature space, such as the distribution information contained in the original data. Finally, the experimental results on the ORL,Yale and FERET face databases demonstrate the superiority of the proposed algorithm compared with other algorithms.
YANG Zhang-Jing,LIU Chuan-Cai,HUANG Pu等. Classification Probability Preserving Discriminant Analysis and Its Application to Face Recognition[J]. , 2014, 27(1): 77-81.
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