An innovative weighted 2 DLDA approach for face recognition

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 andthis may bring large error classification rates. In this paper we proposea 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