| Face recognition using SVM decomposition methods |
| Qiao H(乔红) ; Shaoyan Zhang; Bo Zhang; Keane, J.; Hong Qiao
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| 2004
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| Conference Name | 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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| Source Publication | 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
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| Conference Date | 28 Sept.-2 Oct. 2004
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| Conference Place | Sendai, Japan
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| Abstract | 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. |
| Keyword | 无
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| Document Type | 会议论文
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| Identifier | http://ir.ia.ac.cn/handle/173211/12852
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| Collection | 09年以前成果
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| Corresponding Author | Hong 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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