摘要
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.
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.
出处
《计算机科学》
CSCD
北大核心
2003年第4期11-15,共5页
Computer Science
基金
国家863计划"生物特征识别核心技术与关键问题研究"(项目编号:2001AA114190)
