%0 Journal Article
%T Sub-pattern locality preserving projection for face recognition<br>子模式局部保持映射人脸识别
%A CAO Lin-lin
%A ZHANG Hua-xiang
%A WANG Zhi-chao
%A <br>曹林林
%A 张化祥
%A 王至超
%J 计算机应用研究
%D 2012
%I 
%X Researches show that sub-pattern based face recognition approaches perform better than whole image based methods in local face information preservation. As manifold learning technologies preserve local manifold structure of the nonlinear sub-manifold while implementing dimension reduction, this paper put forward a sub-pattern locality preserving projection BspLPP. Unlike previous approaches partitioned all training images of different classes into sub-images and used the same location images to form a sub-pattern, BspLPP first partitioned the same class images into different sub-images, used the same location sub-images to form a sub-pattern, and then applied LPP to learn the manifold structure of each sub-pattern. Experimental results show that BspLPP preserves the manifold and local information well and improves the recognition performance.
%K face recognition
%K sub-pattern
%K LPP（locality preserving projections）
%K manifold learning<br>人脸识别
%K 子模式
%K 局部保持映射
%K 流形学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=8EA8CD0D0F3BCBFB843C4BA185475ACC&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=708DD6B15D2464E8&sid=6968114F4FC0F855&eid=8BD303BCC0FFE19A&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=15