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PR
2007

Face recognition using spectral features

13 years 10 months ago
Face recognition using spectral features
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 f...
Fei Wang, Jingdong Wang, Changshui Zhang, James T.
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PR
Authors Fei Wang, Jingdong Wang, Changshui Zhang, James T. Kwok
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