Face recognition using SVM decomposition methods

CASIA OpenIR > 09年以前成果

	Face recognition using SVM decomposition methods
	Qiao H(乔红); Shaoyan Zhang; Bo Zhang; Keane, J.; Hong Qiao
	2004
会议名称	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
会议录名称	2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
会议日期	28 Sept.-2 Oct. 2004
会议地点	Sendai, Japan
摘要	Support vector machines (SVM) decomposition methods were proposed to solve high dimensional and/or large data classification problems. Two major decomposition algorithms: Karush-kuhn-Tucker (KKT) condition based algorithm, and `Joachims' decomposition algorithm are popularly adopted. In this paper, both these two decomposition methods are analyzed and applied into face recognition with three basic mapping kernels. Numerical results showed that: a) face recognition with SVM performs better accuracy than other existed methods; b) the decomposition methods can perform face recognition efficiently; c) Joachims' decomposition method has better accuracy than that of decomposition algorithm based on KKT condition; d) linear kernel can provide much higher recognition accuracy than polynomial and slightly better accuracy than Gaussian radial based function (RBF) kernel; Also due to the fact that the linear kernel method is much simpler than others, it is most suitable for face recognition.
关键词	无
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12852
专题	09年以前成果
通讯作者	Hong Qiao
推荐引用方式 GB/T 7714	Qiao H,Shaoyan Zhang,Bo Zhang,et al. Face recognition using SVM decomposition methods[C],2004.