Face Recognition based on multi-class SVM
Support Vector Machine (SVM) provides high performance in generalization, processing small samples, and tackling high-dimensional data. Based on the advantages of SVM, an approach is proposed in this paper, adopting multi-class SVM to realize face recognition. In the approach, Principle Component Analysis (PCA) is used firstly to reduce dimensions so that feature extraction is carried out on face images. Then a method based on One-Versus-All SVM is implemented to realize multi-class classification on feature vectors of the face images. Results of experiments applied to ORL and Yale face databases show that our approach is effective. By the One-Versus-All SVM method, we can respectively obtain recognition rates as high as 93.5％ in ORL face database, and 97.3％ in Yale face database.
face recognition SVM multi-class classification
Zhao Lihong Song Ying Zhu Yushi Zhang Cheng Zheng Yi
College of Information Science and Engineering, Northeastern University, Shenyang, 110004
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5871-5873
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）