%0 Journal Article
%T ICA/NFL Local Face Recognition<br>基于ICA和NFL分类的局部人脸识别方法
%A YE Yi-song
%A WU Yan
%A <br>叶伊松
%A 武妍
%J 中国图象图形学报
%D 2005
%I 
%X A number of current face recognition algorithms use whole face representations found by statistical methods. Independent Component Analysis(ICA) is an example of such methods which is based on signal high-order statistic characteristics. While such unavoidable external factors as illumination, posture and information deformity will cause great changes of gray-scale image data, and eventually will decrease the stability of recognition. This paper presents a local face recognition algorithm that is based on ICA and the nearest feature line (NFL). Firstly, by using manually aligned eye position, segmenting a face image into two parts according to the geometric characteristics of human face, removing hair style and other useless information, then processing principal component analysis (PCA) and ICA for respective parts, and calculating corresponding NFL distance, ultimately processing comprehensive recognition by setting reasonable coefficient of weight. Compared with traditional holistic image representation, this method has many advantages, such as a much higher recognition rate, more stable and flexible in practice. Through a number of experiments, it proves to be an efficient human face recognition algorithm.
%K face recognition
%K principal component analysis (PCA)
%K independent component analysis (ICA)
%K nearest feature line (NFL)<br>识别方法
%K ICA
%K 局部
%K 独立元分析
%K 分类
%K 特征线方法
%K 统计特性
%K 灰度图像
%K 人脸图像
%K 几何特征
%K 特征提取
%K 识别距离
%K 综合判定
%K 稳定性
%K 最近邻
%K 统计性
%K PCA
%K 识别率
%K 块独立
%K 灵活性
%K 信息
%K 截取
%K 人眼
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=53FB6C2E7F739A7F&yid=2DD7160C83D0ACED&vid=F3090AE9B60B7ED1&iid=E158A972A605785F&sid=3FC4D669D19FF0C6&eid=7D1E6EEC2019967D&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=3&reference_num=16