Picture Recognition Using bay WT and KPCA Method
This article an efficient method based on Wavelet Feature and kernel principle component analysis (KPCA) is proposed for face feature selection and a linear support vector machine (SVM) classifier is designed for face recognition. First,using wavelet transform to decompose the face to diverse frequency sub-belts, and divide the eigenvectors into mains and minors which are obtained from low-frequency sub-belts in using KPCA. Then compose the final classifying eigenvectors based on the main and minor eigenvectors. In the end, test in standard face base in order to get the optimal effect. It results this method gets good outcome.
picture recognition wavelet transform (WT) kernel principle component analysis(KPCA), support vector machine (SVM)
CAI Jianli
Department of Automation of Xiamen University
国际会议
武汉
英文
609-612
2007-07-25（万方平台首次上网日期，不代表论文的发表时间）