A Face Recognition Method based on Two-dimensional PCA and SVM

The two-dimensional PCA method extracts feature directly and rapidly in the process of face regnition. However,this method only involes two-dimentional statistical information and lacks useful high-order statistical information for classifying,this may influence the recognition rate. SVM changes the nonlinear question change into the linear question by promoting the dimensions,thus making recognition rate even higher. However,the computational amount is large when this method is used. Thus a new approach in combination with two dimensions PCA and SVM,is proposed to recognize the human face,that is,the 2DPCA is first used to deal with feature extraction,then SVM to make use of the feature to do classification.Experiments with ORL and YALE face-databases show that the proposed method has achieved a higher recognition rate with a reasonable time cost.