A parameterized direct LDA and its application to face recognition

Authors:
Fengxi Song;David Zhang;Jizhong Wang;Hang Liu;Qing Tao
Affiliations:
New Star Research Institute of Applied Technology in Hefei City, Hefei, PR China and Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, PR China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, PR China;New Star Research Institute of Applied Technology in Hefei City, Hefei, PR China;New Star Research Institute of Applied Technology in Hefei City, Hefei, PR China;New Star Research Institute of Applied Technology in Hefei City, Hefei, PR China
Venue:
Neurocomputing
Year:
2007

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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-Loeve 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.