%0 Journal Article
%T Two-dimensional canonical correlation analysis and its application to face recognition
二维典型相关分析及其在人脸识别中的应用
%A SONG Dong-xing
%A LIU Yong-jun
%A CHEN Cai-kou
%A
宋东兴
%A 刘永俊
%A 陈才扣
%J 计算机应用
%D 2008
%I
%X According to the traditional Canonical Correlation Analysis (CCA), a novel method of combined feature extraction called Two-Dimensional Canonical Correlation Analysis (2DCCA) was proposed in this paper. It combines feature matrix directly by using the main idea of image projection in face recognition. Compared with the traditional CCA based on feature vectors, this method has the following two main advantages: first, the Small Sample Size problem (SSS) occurred in traditional CCA is essentially inevitable as a result of the evidently reducing dimension of the covariance matrix. By the same reason, the second advantage is that much computational time would be saved if using the proposed method. Finally, extensive experiments performed on ORL and AR face database verify the effectiveness of the proposed method.
%K Canonical Correlation Analysis (CCA)
%K Two-Dimensional Canonical Correlation Analysis (2DCCA)
%K combined feature extraction
%K face recognition
典型相关分析
%K 二维典型相关分析
%K 特征融合
%K 人脸识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=058C8FA08B8A3AF93EFDFC964B2AE23F&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=9CF7A0430CBB2DFD&sid=76DB0851E3F067C2&eid=A7071151A963A1BF&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=13