%0 Journal Article
%T Minimum-distance discriminant projection and its application to face recognition<br>最小距离鉴别投影及其在人脸识别中的应用
%A Huang Pu
%A Tang Zhenmin
%A <br>黄璞
%A 唐振民
%J 中国图象图形学报
%D 2013
%I 
%X A minimum-distance discriminant projection (MDP)algorithm is proposed to address face recognition problem. Different from the classical linear discriminant analysis (LDA), the MDP is a manifold learning based dimensionality reduction algorithm. MDP first defines the intra-class similarity, weight, and the inter-class weight of each sample. The former one can measure the distance between each data point and the intra-class center, while the latter one does not only characterize the distance between the data point and the inter-class center but also can reflect the relation between the between-class distance and the within-class distance. Then, the high-dimensional data is mapped into a low-dimension space such that the points to within-class center distances are minimized while the points to between-class center distances are maximized simultaneously. At last, experiments on the ORL, FERET, and AR face databases show that the proposed algorithm can outperform other algorithms.
%K face recognition
%K dimensionality reduction
%K linear discriminant analysis
%K locality preserving projections
%K minimum distance<br>人脸识别
%K 降维
%K 线性鉴别分析
%K 局部保持投影
%K 最小距离
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=AE8DAD4245F3E9A97A62E1F077DFBDA0&yid=FF7AA908D58E97FA&vid=13553B2D12F347E8&iid=0B39A22176CE99FB&sid=BC084ACE66B62CC8&eid=9F6DA927E843CD50&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=14