A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.
A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) ; 2 ; 1083-1088 vol. 2
2005-01-01
214310 byte
Conference paper
Electronic Resource
English
Two-Dimensional Fisher Discriminant Analysis and Its Application to Face Recognition
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