Random Sampling LDA for Face Recognition

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Random Sampling LDA for Face Recognition
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 are preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating ...
Xiaogang Wang, Xiaoou Tang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Xiaogang Wang, Xiaoou Tang
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