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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Multilinear Nonparametric Feature Analysis
verfasst von : Xu Zhang, Xiangqun Zhang, Jian Cao, Yushu Liu
Erschienen in: Computer Vision – ACCV 2009
Verlag: Springer Berlin Heidelberg
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A novel method with general tensor representation for face recognition based on multilinear nonparametric discriminant analysis is proposed. Traditional LDA-based methods suffer some disadvantages such as small sample size problem (SSS), curse of dimensionality, as well as a fundamental limitation resulting from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. In addition, traditional LDA-based methods and their variants don’t consider the class boundary of samples and interior structure of each sample class. To address the problems, a new multilinear nonparametric discriminant analysis is proposed, and new formulations of scatter matrices are given. Experimental results indicate the robustness and accuracy of the proposed method.