A Modified Method for Face Recognition Using SVM

Support vector machine is a new technique of machine learning developed from the middle of 1990s. There are two important things in the process of face recognition using SVM. One is the feature vectors extracted from face images, the other is the classifier we choose. Our approach optimizes traditional method from these two points. The adaptively weighted Fisherface purposes on a better feature extraction; the size condensing of training data is used to speed up the modeling of the SVMs; and binary tree structure SVMs is selected here to extend SVM to multi-class cases without much additional complexity. We do our experiments on ORL face database and get good recognition results. The accuracy of recognition results are compared to show the influence of the dimension of the Fisherface. At last, the benefit of adaptively weighted Fisherface is shown by a compare with the traditional Fisherface.