Kernel maximum scatter difference based feature extraction and its application to face recognition

Authors:
Jian-guo Wang;Yu-sheng Lin;Wan-kou Yang;Jing-yu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China and Network and Education Center, Tangshan College, Tangshan 063 ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

This paper formulates maximum scatter difference (MSD) criterion in the kernel-including feature space and develops a two-phase kernel maximum scatter difference criterion: KPCA plus MSD. The proposed method first maps the input data into a potentially much higher dimensional feature space by virtue of nonlinear kernel trick, and in such a way, the problem of feature extraction in the nonlinear space is overcome. Then the scatter difference between between-class and within-class as discriminant criterion is defined on the basis of the above computation; therefore, the singularity problem of the within-class scatter matrix due to small sample size problem occurred in classical Fisher discriminant analysis is avoided. The results of experiments conducted on a subset of FERET database, Yale database indicate the effectiveness of the proposed method.