Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14994
Title: A parameterized direct LDA and its application to face recognition
Authors: Song, F
Zhang, D 
Wang, J
Liu, H
Tao, Q
Keywords: Direct linear discriminant analysis
Feature extraction
Karhunen-Loève expansion
Large-scale face recognition
Weight coefficient
Issue Date: 2007
Publisher: Elsevier
Source: Neurocomputing, 2007, v. 71, no. 1-3, p. 191-196 How to cite?
Journal: Neurocomputing 
Abstract: In this paper, we propose a new feature extraction method-parameterized direct linear discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D-LDA), PD-LDA is a modification of KLB (the Karhunen-Loève expansion based on the between-class scatter matrix). As an improvement of D-LDA and KLB, PD-LDA inherits two important advantages of them. That is, it can be directly applied to high-dimensional input spaces and implemented with great efficiency. Meanwhile, experimental results conducted on two benchmark face image databases, i.e., AR and FERET, demonstrate that PD-LDA is much more effective and robust than D-LDA. In addition, it outperforms state-of-the-art facial feature extraction methods such as KLB, eigenfaces, and Fisherfaces.
URI: http://hdl.handle.net/10397/14994
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2007.01.003
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