An innovative weighted 2DLDA approach to face recognition - Lu, C.; An, Senjian; Liu, Wan-Quan; Liu, X. (Springer, 2011)
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 data-bases such as the ORL database, the Yale database, the YaleB database and the Feret database in comparison with other 2DLDA variants.