%0 Journal Article
%T Method based on wavelet multiresolution analysis and KPCA for face recognition<br>基于小波分析与KPCA的人脸识别方法
%A LI Wei-hong
%A GONG Wei-guo
%A CHENG Wei-ming
%A LIANG Yi-xiong
%A YING Ke-zhong
%A <br>李伟红
%A 龚卫国
%A 陈伟民
%A 等
%J 计算机应用
%D 2005
%I 
%X Feature selection is very important for face recognition.The valuable facial low-frequency features can be obtained by wavelet multiresolution analysis and the non-linear features kernel principle component analysis(KPCA) can be extracted from initial face images.In this paper,an efficient method based on wavelet multiresolution analysis and KPCA was proposed for face feature selection and a linear support vector machine(SVM) classifier was designed for face recognition.Experimental results on UMIST face databases indicate the effectiveness of the proposed method.
%K face recognition
%K Wavelet Transform(WT)
%K Kernel Principle Component Analysis(KPCA)
%K Support Vector Machine(SVM)<br>人脸识别
%K 小波变换（WT）
%K 核主元分析（KPCA）
%K 支持向量机（SVM）
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=6604E212122B7105&yid=2DD7160C83D0ACED&vid=C5154311167311FE&iid=F3090AE9B60B7ED1&sid=F44D606ED7E8033D&eid=D0293DFC566BF929&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=11