Study of Face Recognition Algorithm Base on Proximal Support Vector Machine

摘要：

Face recognition algorithm based on support vector machines(SVM)have better recognition rate,but the time of training is very long when it have a large number of sample. To overcome this shortcoming,in this paper,the face recognition algorithm based on the proximal SVM (PSVM) was proposed,which the first face image through principal component analysis(PCA)for dimensionality reduction and then use PSVM to classify.The experimental results in ORL and Yale face database show that the training time had a greater reduction and the recognition rate slight lower than the traditional SVM.The reduction of training time is SVMs a few percent.In particular,it has better improve of training time when its dimension is not high and have a larger number of samples.

关键词： PSVM face recognition PCA training time

作者: Liying Lang Feijia Xia Xiaojie Wang

作者单位: Hebei University of Engineering Handan,Hebei,P.R.China College of Information and Electrical Engineering,Hebei University of Engineering Handan,Hebei,P.R.C Department of Ship Engineering,Weihai Voeationa College Weihai,Shandong,P.R.China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 702-705

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）