Please use this identifier to cite or link to this item:
Title: BDPCA plus LDA : a novel fast feature extraction technique for face recognition
Authors: Zuo, Wangmeng
Zhang, David D.
Yang, Jian
Wang, Kuanquan
Subjects: 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.
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.
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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Journal/Magazine Article
DOI: 10.1109/TSMCB.2005.863377
ISSN: 1083-4419
Appears in Collections:COMP Journal/Magazine Articles

Files in This Item:
File Description SizeFormat 
SMCB_C_36_4_06.pdf361.39 kBAdobe PDFView/Open

All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.