%0 Journal Article
%T Face Recognition Based on Kernel Fisher Nonlinear Optimal Discriminant Analysis<br>基于核的Fisher非线性最佳鉴别分析在人脸识别中的应用
%A CHENG Xin-min
%A JIANG Yun-liang
%A HU Wen-jun
%A WU Xiao-hong
%A <br>成新民
%A 蒋云良
%A 胡文军
%A 吴小红
%J 中国图象图形学报
%D 2007
%I 
%X Extracting the most discriminatory features is important in face recognition tasks. In the case of a small number of face samples, as the existed methods for extracting nonlinear most discriminatory face features encounter various problems. So a new method for extracting fisher nonlinear most discriminatory features is proposed in this paper. The fisher criterion is formulated using between-class scatter matrix and within-class scatter matrix based on kernel method. Thus nonlinear most discriminatory features are obtained. However, this method causes ill-problem. To solve this problem, we search optimal discriminant vectors in null space of within-class scatter matrix. Repeated experimental results on ORL database indicate that the proposed method significantly outperforms the Fisher linear discriminant analysis(FLDA) and generalized discriminant analysis(GDA).
%K face recognition
%K Fisher nonlinear discriminant analysis
%K kernel method
%K small sample size problem
%K ill-pose problem<br>人脸识别
%K Fisher非线性鉴别分析
%K 核方法
%K 小样本问题
%K 病态问题
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=6DF3347945959F5C&yid=A732AF04DDA03BB3&vid=59906B3B2830C2C5&iid=5D311CA918CA9A03&sid=98973A2DBA64FAC9&eid=14475B1A66930D94&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=13