NDLI: Dual-space linear discriminant analysis for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Xiaogang Wang Xiaoou Tang
Copyright Year	2004
Description	Author affiliation: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, China (Xiaogang Wang; Xiaoou Tang)
Abstract	Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they are often unstable and have to discard some discriminative information. In this paper, a dual-space LDA approach for face recognition is proposed to take full advantage of the discriminative information in the face space. Based on a probabilistic visual model, the eigenvalue spectrum in the null space of within-class scatter matrix is estimated, and discriminant analysis is simultaneously applied in the principal and null subspaces of the within-class scatter matrix. The two sets of discriminative features are then combined for recognition. It outperforms existing LDA approaches.
Sponsorship	IEEE Comput. Soc
File Size	264680
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315214
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear discriminant analysis Face recognition Null space Principal component analysis Scattering Eigenvalues and eigenfunctions Feature extraction Spatial databases Vectors Computer Society
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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