%0 Journal Article
%T Face Recognition Based on Kernel Discriminative Common Vectors<br>基于核鉴别共同矢量的小样本脸像鉴别方法
%A He Yun-hui
%A Zhao Li
%A Zou Cai-rong
%A <br>贺云辉
%A 赵 力
%A 邹采荣
%J 电子与信息学报
%D 2006
%I 
%X Face recognition tasks always encounter Small Sample Size (SSS) problem, which leads to the ill-posed problem in Fisher Linear Discriminant Analysis (FLDA). The Discriminative Common Vector (DCV) successfully overcomes this problem for FLDA. In this paper, the DCV is extended to nonlinear case, by performing the Gram-Schmidt orthogonalization twice in feature space, which involving computing two kernel matrices and performing a Cholesky decomposition of a kernel matrix. The experimental results demonstrate that the proposed KDCV achieve better performance than the DCV method.
%K Face Recognition
%K Discriminative Common Vectors
%K Kernel method
%K Small Sample Size (SSS) problem
%K Fisher Linear Discriminant Analysis (FLDA)<br>人脸识别
%K 鉴别共同矢量
%K 核方法
%K 小样本问题
%K Fisher线性鉴别分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=49469C306E79D1B4&yid=37904DC365DD7266&vid=D3E34374A0D77D7F&iid=59906B3B2830C2C5&sid=44F41EAA25AF5306&eid=9EFA9C0344D40E4A&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=10