Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/240
Title: BDPCA plus LDA : a novel fast feature extraction technique for face recognition
Authors: Zuo, W
Zhang, DD 
Yang, J
Wang, K
Keywords: Bidirectional principal component analysis (BDPCA)
Face recognition
Feature extraction
Linear discriminant analysis (LDA)
Principal component analysis (PCA)
Issue Date: Aug-2006
Publisher: IEEE
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Aug. 2006, v. 36, no. 4, p. 946-953 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called “small sample size” (SSS) problem. One popular solution to the SSS problem is principal component analysis (PCA) + LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effective. In this correspondence, we proposed a novel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA + LDA), which performs an LDA in the BDPCA subspace. Two face databases, the ORL and the Facial Recognition Technology (FERET) databases, are used to evaluate BDPCA + LDA. Experimental results show that BDPCA + LDA needs less computational and memory requirements and has a higher recognition accuracy than PCA + LDA.
URI: http://hdl.handle.net/10397/240
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2005.863377
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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