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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    Titel :

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


    Beteiligte:
    Kong, H. (Autor:in) / Wang, L. (Autor:in) / Teoh, E.K. (Autor:in) / Wang, J.-G. (Autor:in) / Venkateswarlu, R. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    214310 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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