Title

A framework of 2D fisher discriminant analysis: application to face recognition with small number of training samples

RIS ID

54399

Publication Details

Kong, H., Wang, L., Teoh, E. Khwang., Wang, J. & Venkateswarlu, R. (2005). A framework of 2D fisher discriminant analysis: application to face recognition with small number of training samples. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1083-1088). Australia: IEEE.

Abstract

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.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/CVPR.2005.30