Fusion of SVD and LDA for face recognition

A face recognition method based on the fusion of linear discriminant analysis (LDA) and singular value decomposition (SVD) is presented. In theory, fusion of different data or classifiers can achieve better performance when they are independent of each other or they can overcome shortcomings of each other. As one of the subspace methods, LDA-based method has a drawback that LDA is sensitive (variant) to translation, rotation and other geometric transforms. SVD-based method, as an algebraic feature extraction approach, has the merit of invariance to translation, rotation and mirror transforms. By combining these two methods, it is expected that better recognition performance can be obtained. Experiment results on ORL face database show the effectiveness of the proposed method.