多分类器融合的人脸识别与身份认证
Fusion of Multiple Classifiers for Face Recognition and Person Authentication

董火明 ¹ 高隽 ² 汪荣贵 ¹

文摘	人脸识别是生物特征识别技术中一个活跃的研究领域,取得了很多实践成果,但是单一分类器一般不能取得满意的识别率与身份认证效果.本文采用贝叶斯决策理论分析了常见的积、和、中值以及投票多分类器融合方法,并根据实际的选举情形,对投票法进行了2种改进.然后对协同人脸识别、特征脸法以及复合方法等人脸识别分类器进行决策层的融合,对ORL库中人脸识别仿真实验表明:文中的多分类器融合的人脸识别方法具有较好的分类性能,对污损、低分辨率人脸图像具有可靠的识别率、鲁棒性强,而且应用于人脸身份认证中取得了较好的认证效果.
其他语种文摘	Face recognition is an active subject in the area of biometrics. Lots of achievements have been obtained using face recognition techniques, but generally we can't get high recognition rate and satisfied person authentication effect using Single classifier. This paper discusses the famili?r fusion methods like product, sum, median and vote rules according to Bayesian theory. And two modified vote rules are presented comparing with election in the real world. Then, these methods are used for decision-fusion of multiple face recognition classifiers: synergetic face recognition classifier, eigenfaces classifier and composite-approach classifier. Experiments with face samples in ORL databases show that the proposed face recognition method has good classifying Performance, reliable recognition rate, and strong robustness to defiled and low-resolution face images. More important is that the proposed method also has satisfied experimental results in face person authentication.
来源	系统仿真学报 ,2004,16(8):1849-1853 【核心库】
关键词	人脸识别 ; 多分类器融合 ; 身份认证 ; 协同识别 ; 特征脸法
地址	1. 合肥工业大学计算机与信息学院图像信息处理研究室, 合肥, 230009 2. 中国科学院合肥智能机械研究所, 合肥, 230031
语种	中文
文献类型	研究性论文
ISSN	1004-731X
学科	自动化技术、计算机技术
基金	国家自然科学基金 ; 安徽省优秀青年科技基金
文献收藏号	CSCD:1805155