Studies on human face recognition based on greedy kernel principal component analysis

A human face recognition algorithm based on greedy kernel principal component analysis (GKPCA) is presented to meet the requirement of quick face recognition on line. In the algorithm, typical human face are decomposed by fast wavelet transform(FWT), then the greedy algorithm is used to reduce training set and the features of the low frequency sub-images are extracted by kernel principal component analysis(KPCA). Consequently, the features extracted are recognized by support vector machine (SVM). Simulations of the algorithm proposed on the basis of ORL (Olivetti Research Lab) face database and NORL face databases show that the algorithm is capable of reducing training time with high recognition rate.