Face Recognition Based on Principle Component Analysis and Support Vector Machine

Face recognition is an important research field of pattern recognition.Up to now,it caused researchers great concern from these fields,such as pattern recognition,computer vision,and physiology,and so on.Various recognition algorithms have been proposed. Generally,we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify them into a class accurately.Therefore,it is necessary for us to pay close attention to feature extractor and classifier.In this paper, in order to raise recognition rate,Principle Component Analysis (PCA) is used to extract image feature,and Support Vector Machine (SVM) is used to deal with face recognition problem. SVM has been recently proposed as a new classifier for pattern recognition.We take Principle Component Analysis & Support Vector Machine (PCA&SVM) to do experiments on the Cambridge ORL Face database,and compare this method with Principle Component Analysis & Nearest Neighbor (PCA&NN) and Support Vector Machine (SVM) on recognition rate and recognition time respectively.Finally,this experimental results show that recognition rate of this method,under small samples circumstance,is better than other two methods. It shows that,for face recognition,sending PCA features to SVM classifiers is feasible and correct.