An adaptive nonparametric discriminant analysis method and its application to face recognition

Authors:
Liang Huang;Yong Ma;Yoshihisa Ijiri;Shihong Lao;Masato Kawade;Yuming Zhao
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Sensing & Control Technology Lab., Omron Corporation, Kyoto, Japan;Sensing & Control Technology Lab., Omron Corporation, Kyoto, Japan;Sensing & Control Technology Lab., Omron Corporation, Kyoto, Japan;Sensing & Control Technology Lab., Omron Corporation, Kyoto, Japan;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Year:
2007

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Abstract

Linear Discriminant Analysis (LDA) is frequently used for dimension reduction and has been successfully utilized in many applications, especially face recognition. In classical LDA, however, the definition of the between-class scatter matrix can cause large overlaps between neighboring classes, because LDA assumes that all classes obey a Gaussian distribution with the same covariance. We therefore, propose an adaptive nonparametric discriminant analysis (ANDA) algorithm that maximizes the distance between neighboring samples belonging to different classes, thus improving the discriminating power of the samples near the classification borders. To evaluate its performance thoroughly, we have compared our ANDA algorithm with traditional PCA+LDA, Orthogonal LDA (OLDA) and nonparametric discriminant analysis (NDA) on the FERET and ORL face databases. Experimental results show that the proposed algorithm outperforms the others.