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
Conference Name2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Source Publication2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
Conference Date28 Sept.-2 Oct. 2004
Conference PlaceSendai, Japan
AbstractSupport 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.
Keyword
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12852
Collection09年以前成果
Corresponding AuthorHong Qiao
Recommended Citation
GB/T 7714
Qiao H,Shaoyan Zhang,Bo Zhang,et al. Face recognition using SVM decomposition methods[C],2004.
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