Face recognition using spectral features

Authors:
Fei Wang;Jingdong Wang;Changshui Zhang;James Kwok
Affiliations:
Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, PR China;Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, PR China;Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Venue:
Pattern Recognition
Year:
2007

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Abstract

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a key problem that significantly affects the recognition results. In this paper, we propose a new unsupervised nonlinear feature extraction method called spectral feature analysis (SFA). The main advantages of SFA over traditional feature extraction methods are: (1) SFA does not suffer from the small-sample-size problem; (2) SFA can extract discriminatory information from the data, and we show that linear discriminant analysis can be subsumed under the SFA framework; (3) SFA can effectively discover the nonlinear structure hidden in the data. These appealing properties make SFA very suitable for face recognition tasks. Experimental results on three benchmark face databases illustrate the superiority of SFA over traditional methods.