Multilinear nonparametric feature analysis

Authors:
Xu Zhang;Xiangqun Zhang;Jian Cao;Yushu Liu
Affiliations:
Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing;School of Computer Science and Technology, Xuchang University, Xuchang;Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing;Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

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Abstract

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.