An Innovative Weighted 2DLDA Approach for Face Recognition

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 produce a large erroneous classification rate. In this paper we propose a new 2DLDA-based approach that can overcome such drawback for the existing 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, which eventually leads to an optimal projection matrix. Experimental results show that the proposed approach can improve the recognition rates on benchmark databases such as the ORL database, the Yale database, the YaleB database and the Feret database in comparison with other 2DLDA variants.