| 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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会议名称 | 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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会议录名称 | 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
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会议日期 | 28 Sept.-2 Oct. 2004
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会议地点 | Sendai, Japan
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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
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文献类型 | 会议论文
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条目标识符 | http://ir.ia.ac.cn/handle/173211/12852
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专题 | 09年以前成果
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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.
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