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An Application Research On Face Recognition Based On KPCA Feature Extract And SVM

Posted on:2007-05-10Degree:MasterType:Thesis
Country:ChinaCandidate:H WangFull Text:PDF
GTID:2178360182986625Subject:Computer software and theory
Abstract/Summary:PDF Full Text Request
Because of the theoretical maturity, global optimization, excellent generalization, the support vector machine method is becoming the hot spot in the field of artificial intelligence. Kernel Principal Component Analysis method have the characteristics of feature extraction rapidity, sufficient feature information reservation, so it is taken more seriously by many researchers. This article combines the two methods in the field of face recognition. The main work of the thesis is summarized as follows:(1) Reorganized and summarized the domestic and foreign academic circles about the research results of the statistical learning theory and the kernel principal component analysis. Introduced the basic concept of statistics learning theory and the basic principle of support vector machine as well as the kernel principal component analysis basic thought.(2) Studied the basic principle of SVM, Layer-SVM, the principal component analysis as well as the kernel principal component analysis, and analyzed their good and bad points respectively, finally unified the multi-classification method of Layer-SVM and the feature extraction method of kernel principal component analysis and applied it in the experiment.(3) This thesis used the kernel principal component analysis to extract face image feature, then carries on the recognition using support vector machine to it in the experiment. Experimental results demonstrated that this method can obtain good recognition effect, also reduce the training time of human face recognition.
Keywords/Search Tags:Normalization, Principal Component Analysis, Kernel Principal Component Analysis, Support Vector Machine, Kernel Function, Face Recognition
PDF Full Text Request
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