NDLI: Random sampling LDA 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. Fisherface and null space LDA (N-LDA) are two conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing different overfitting problems for the two kinds of LDA classifiers, we propose an approach using random subspace and bagging to improve them respectively. By random sampling on feature vector and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed. The two kinds of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating shape, texture and Gabor responses is finally developed.
Sponsorship	IEEE Comput. Soc
File Size	279350
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315172
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	Sampling methods Linear discriminant analysis Face recognition Null space Principal component analysis Bagging Feature extraction Scattering Eigenvalues and eigenfunctions Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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