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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    Title :

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


    Contributors:
    Kong, H. (author) / Wang, L. (author) / Teoh, E.K. (author) / Wang, J.-G. (author) / Venkateswarlu, R. (author)


    Publication date :

    2005-01-01


    Size :

    214310 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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