增强的无监督人脸鉴别技术  

Enhanced unsuperised discriminant method for face recognition

作　　者：黄璞[1] 陈才扣[1] 

机构地区：[1]扬州大学信息工程学院,江苏扬州225009

出　　处：《计算机工程与应用》2010年第18期167-169,173,共4页Computer Engineering and Applications

基　　金：国家自然科学基金 No.60875004;江苏省高校自然科学基金(No.07KJB520133)~~

摘　　要：增强的独立分量分析(EICA)是一种基于样本整体特征的无监督特征抽取方法,并没有考虑样本的局部特征,因此EICA不利于处理人脸识别这类非线性问题的。无监督鉴别投影技术(UDP)用于高维数据压缩,其基本思想是寻找一组有效的投影方向,使得样本投影后,局部散度最小同时非局部散度最大。UDP同时考虑到样本的局部特征和非局部特征,能够反映样本内在的数据关系,因此UDP能够对样本有效地分类。提出了一种增强的无监督人脸鉴别技术,该方法结合了EICA和UDP的优点,能够:(1)反映样本高阶统计特征;(2)发掘样本内在的几何结构,从而有利于分类。在Yale人脸库和FERET人脸库上的实验验证了该算法的有效性。Enhanced Independent Component Analysis（EICA） is an unsupervised feature extraction method which is presented based on the overall characteristics,so EICA doesn＇t fit to solve such a nonlinear problem as face recognition.Unsupervised Discriminant Projection（UDP） technique is developed for dimensionality reduction of high-dimensional data,and it considers both the local characteristics and non-local characteristics,thus UDP is effective for classification of samples.In this paper,an enhanced unsupervised method is introduced,which has advantages of both EICA and UDP as：（1）it can reflect high-order statistics of samples （2）it is able to discover essential data structure,and obtain a set of effective discriminant projection axis for classification.The experiments on the Yale and FERET databases validate the effectiveness of the proposed method.

关 键 词：局部特征 非局部特征 独立分量分析 无监督投影鉴别 特征抽取 人脸识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]