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.


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    Title :

    An Adaptive Nonparametric Discriminant Analysis Method and Its Application to Face Recognition


    Contributors:
    Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Huang, Liang (author) / Ma, Yong (author) / Ijiri, Yoshihisa (author) / Lao, Shihong (author) / Kawade, Masato (author) / Zhao, Yuming (author)

    Conference:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Published in:

    Computer Vision – ACCV 2007 ; Chapter : 67 ; 680-689


    Publication date :

    2007-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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