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A improved method of feature extraction based on kernel maximum margin criterion (KMMC) is presented for face recognition in this paper, i.e. a simple algorithm of uncorrected optimal discriminant vectors in kernel feature space is proposed for nonlinear feature extraction. The proposed method has more powerful capability to eliminate the statistical correlation between feature vectors and its mathematical formulation is simple. Experimental results on ORL face database and YALE face database show that the new method is better than KMMC and kernel principal component analysis (KPCA) in terms of recognition rate.
Natural Computation, 2007. ICNC 2007. Third International Conference on (Volume:1 )
Date of Conference: 24-27 Aug. 2007