%0 Journal Article
%T Supervised locality preserving projection and tensor decomposition for multi-view face recognition<br>基于SLPP和张量分解相结合的人脸识别*
%A XU Yi-nan
%A WANG Shi-tong
%A <br>许亦男
%A 王士同
%J 计算机应用研究
%D 2011
%I 
%X To solve the incapability of multi-linear analysis algorithm with multi-view face images, this paper proposed an improved algorithm . A supervised locality preserving projection that accurately described the nonlinearity in pose embedding space was introduced to project the face images into a low dimensional subspace. In the SLPP algorithm the neighboring points as supervised information could find the global structure as well as local structure. Combining with tensor decomposition and kernel methods, it well learned a set of nonlinear mapping functions from the embedding space into the input space. The proposed method was evaluated on Oriental Face database of multi-view face images in comparison to the other subspace methods. Experimental results show the effectiveness of the new method.
%K supervised locality preserving projection(SLPP)
%K tensor decomposition
%K kernel function
%K view manifold
%K face recognition<br>有监督的局部保留投影
%K 张量分解
%K 核函数
%K 姿态流形
%K 人脸识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F32C7DEF351C88CCEB324E8FF3F823BB&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=5D311CA918CA9A03&sid=02EADF0628EF4A34&eid=03C77768D2DED7AC&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12