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融合全局与局部特征的子空间人脸识别算法
引用本文:王蕴红,范伟,谭铁牛.融合全局与局部特征的子空间人脸识别算法[J].计算机学报,2005,28(10):1657-1663.
作者姓名:王蕴红  范伟  谭铁牛
作者单位:[1]中国科学院自动化研究所模式识别国家重点实验室,北京100080 [2]北京航空航天大学计算机学院,北京100083
基金项目:本课题得到国家自然科学基金(60332010,60335010)和国家“九七三”重点基础研究发展规划项目基金(2004CB318100)资助.
摘    要:文章的工作基于子空间分析框架,从特征融合的角度模拟人类视觉系统的自适应识别功能进行人脸识别.首先,利用主成分分析(Principal Component Analysis,PCA)提取人脸全局特征,在一个低维的“人脸子空间”中依照最近邻法则匹配测试样本;然后,针对人脸局部特征,提出了一种根据各局部子块(如眉、眼、鼻、嘴)的特征偏离程度进行自动加权的算法;最后,基于模糊综合的原理对全局与局部特征进行数据融合,给出最终识别结果.实验表明,该算法能很好地结合人脸图像全局和局部的互补信息,识别效果优于各单一模块的分类性能.

关 键 词:人脸识别  主成分分析  局部特征  全局特征  模糊综合
收稿时间:2004-03-18
修稿时间:2004-03-182005-06-09

Face Recognition Based on Information Fusion
WANG Yun-Hong, FAN Wei,AN Tie-Niu.Face Recognition Based on Information Fusion[J].Chinese Journal of Computers,2005,28(10):1657-1663.
Authors:WANG Yun-Hong  FAN Wei  AN Tie-Niu
Affiliation:1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080;2 School of Computer Science and Engineering, Beihang University, Beijing 100083
Abstract:In this paper, a method based on the fusion of global and local facial features in the framework of subspace analysis for face recognition is proposed. PCA (Principal Component (Analysis)) is performed to extract global features, and the results are then sent to a NN (Nearest-Neighbor) classifier for recognition. A special strategy is used to combine different local features such as eyes, eyebrows, nose and mouth according to their respective salience. The idea of FI (fuzzy integration) is adopted to fuse both global and local features and the final result is given. The experiments on the NLPR database demonstrate the effectiveness and feasibility of the proposed method.
Keywords:face recognition  principle component analysis  local feature  global feature  fuzzy integration
本文献已被 CNKI 维普 万方数据 等数据库收录!
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