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.
会议录
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
Qiao H,Shaoyan Zhang,Bo Zhang,et al. Face recognition using SVM decomposition methods[C]. 见:2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, Japan. 28 Sept.-2 Oct. 2004.
; Shaoyan Zhang; Bo Zhang; Keane, J.
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