Face feature selection with binary particle swarm optimization and support vector machine

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作者：
机构：
[1] Yin, Hongtao
[2] Qiao, Jia Qing
[3] Fu, Ping
[4] Xia, Xin Yuan
来源：
Yin, Hong Tao (yinht@hit.edu.cn) | 1600年 / Ubiquitous International卷 / 05期
关键词：
Particle swarm optimization (PSO) - Feature Selection - Discrete cosine transforms - Support vector machines;
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摘要：
A face feature selection and recognition method based on BPSO and SVMWrapper model is presented. To solve the problem that DCT coefficients dimension is higher for face recognition, we design a SVM-Wrapper model based on BPSO. In the processof training SVM, the cross-validation is used to training samples, and the recognition accuracy is used for defining the fitness function of BPSO feature selection algorithm. The fitness function is used to guide the BPSO algorithm to search the optimal feature subset. The experiments on ORL databases show that the improved method is effective. © 2014.
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