Rapid and brief communication: Inverse Fisher discriminate criteria for small sample size problem and its application to face recognition

Authors:
Xiao-Sheng Zhuang;Dao-Qing Dai
Affiliations:
Department of Mathematics, Faculty of Mathematics and Computing, Sun Yat-Sen (Zhongshan) University, Guangzhou 510275, China;Department of Mathematics, Faculty of Mathematics and Computing, Sun Yat-Sen (Zhongshan) University, Guangzhou 510275, China
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper addresses the small sample size problem in linear discriminant analysis, which occurs in face recognition applications. Belhumeur et al. [IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (1997) 711-720] proposed the FisherFace method. We find out that the FisherFace method might fail since after the PCA transform the corresponding within class covariance matrix can still be singular, this phenomenon is verified with the Yale face database. Hence we propose to use an inverse Fisher criteria. Our method works when the number of training images per class is one. Experiment results suggest that this new approach performs well.