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Graph-Optimized Line Discriminant Analysis for Face Recognition

A Graph-optimized Linear Discriminant Analysis (GLDA) for face recognition is proposed, which redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the not-same-class points to the penalty graph by a strictly monotone decreasing function. Experiments on Yale, YaleB, UMIST face dataset are provided for demonstrating our results.