Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (2004)
Washington, D.C., USA
June 27, 2004 to July 2, 2004
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.200
Xiaogang Wang , Chinese University of Hong Kong
Xiaoou Tang , Chinese University of Hong Kong
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 shape, texture and Gabor responses is finally developed.
X. Wang and X. Tang, "Random Sampling LDA for Face Recognition," Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.(CVPR), Washington, D.C., USA, 2004, pp. 259-265.