caa a22 4500 467914605 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s11263-006-8098-z doi (NATIONALLICENCE)springer-10.1007/s11263-006-8098-z Random Sampling for Subspace Face Recognition [Elektronische Daten] [Xiaogang Wang, Xiaoou Tang] Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed. Springer Science + Business Media, LLC, 2006 random subspace method nationallicence bagging nationallicence LDA nationallicence face recognition nationallicence subspace analysis nationallicence Wang Xiaogang Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong aut Tang Xiaoou Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong aut International Journal of Computer Vision Kluwer Academic Publishers 70/1(2006-10-01), 91-104 0920-5691 70:1<91 2006 70 11263 https://doi.org/10.1007/s11263-006-8098-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-8098-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Xiaogang Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong aut NATIONALLICENCE 700 1- Tang Xiaoou Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 70/1(2006-10-01), 91-104 0920-5691 70:1<91 2006 70 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer