An Innovative Weighted 2DLDA Approach for Face Recognition

Authors:
Chong Lu;Senjian An;Wanquan Liu;Xiaodong Liu
Affiliations:
School of Electronic and Information Engineering DLUT, Dalian, China 116024 and YiLi Normal College, Yining, China 835000;Curtin University of Technology, Perth, Australia 6102;Curtin University of Technology, Perth, Australia 6102;School of Electronic and Information Engineering DLUT, Dalian, China 116024
Venue:
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2009

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Abstract

Two Dimensional Linear Discrimination Analysis (2DLDA) is an effective feature extraction approach for face recognition, which manipulates on the two dimensional image matrices directly. However, some between-class distances in the projected space are too small and this may bring large error classification rates. In this paper we propose a new 2DLDA-based approach that can overcome such drawback in the 2DLDA. The proposed approach redefines the between-class scatter matrix by putting a weighting function based on the between-class distances, and this will balance the between-class distances in the projected space iteratively. In order to design an effective weighting function, the between-class distances are calculated and then used to iteratively change the between-class scatter matrix. Experimental results showed that the proposed approach can improve the recognition rates on the ORL database, the Yale database and the YaleB database in comparison with other 2DLDA variants.