Support Vector Machines are a binary classification method and have demonstrated excellent results in pattern recognition. Face recognition is a multi-class problem, where the number of classes is of the known individ...Support Vector Machines are a binary classification method and have demonstrated excellent results in pattern recognition. Face recognition is a multi-class problem, where the number of classes is of the known individuals. In this paper we use face data extracted from Eigenfeatures and develope a method to extend SVM to using in multi-class. The training set consists of 5 images of each of the 50 persons equally distributed among frontal, approximately 15°rotated respectively, and the test set consists of 10 images each of the 50 persons. In the ICT-YC face gallery, the proposed system obtains competitive results highly: a correct recognition rate of 94.8％ for all the 50 persons, to the less number of the persons and to the famous ORL face gallery we also get good face recognition rate.展开更多
文摘Support Vector Machines are a binary classification method and have demonstrated excellent results in pattern recognition. Face recognition is a multi-class problem, where the number of classes is of the known individuals. In this paper we use face data extracted from Eigenfeatures and develope a method to extend SVM to using in multi-class. The training set consists of 5 images of each of the 50 persons equally distributed among frontal, approximately 15°rotated respectively, and the test set consists of 10 images each of the 50 persons. In the ICT-YC face gallery, the proposed system obtains competitive results highly: a correct recognition rate of 94.8％ for all the 50 persons, to the less number of the persons and to the famous ORL face gallery we also get good face recognition rate.
