An innovative weighted 2DLDA approach to face recognition
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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.
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Lu, C.; An, Senjian; Liu, Wan-quan; Liu, X. (2009)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 ...
Nguyen, Nam; Liu, Wan-Quan; Venkatesh, Svetha (2008)Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its ...
Xue, Mingliang; Liu, Wan-Quan; Liu, X. (2013)Fuzzy linear discriminate analysis (FLDA), the principle of which is the remedy of class means via fuzzy optimization, is proven to be an effective feature extraction approach for face recognition. However, some of the ...