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基于分形编码和奇异值分解的混合人脸识别方法
引用本文:曹林,刘小军,邹谋炎.基于分形编码和奇异值分解的混合人脸识别方法[J].电子与信息学报,2005,27(4):544-547.
作者姓名:曹林  刘小军  邹谋炎
作者单位:中国科学院电子学研究所,北京,100080
基金项目:中国科学院知识创新工程项目
摘    要:该文提出了一种新的基于分形编码的人脸识别方法。在分形近邻距离的基础上,提出了分形奇异值近邻距 离,并把分形编码和局部奇异值分解结合起来,提高了识别率。实验结果表明,与仅仅使用分形近邻距离相比,该 算法对光照变化、表情和姿态变化具有更大的容忍度,而且训练时间短,识别率高。

关 键 词:人脸识别  分形编码  局部奇异值分解  分形奇异值近邻距离
文章编号:1009-5896(2005)04-0544-04
收稿时间:2003-12-12
修稿时间:2003年12月12

A Hybrid Method for Face Recognition Based on Fractal Coding and Singular Value Decomposition
Cao Lin,Liu Xiao-jun,ZOU Mou-yan.A Hybrid Method for Face Recognition Based on Fractal Coding and Singular Value Decomposition[J].Journal of Electronics & Information Technology,2005,27(4):544-547.
Authors:Cao Lin  Liu Xiao-jun  ZOU Mou-yan
Affiliation:Institute of Electronics Chinese Academy of Sciences Beijing 100080 China
Abstract:In this paper, a new face recognition method based on fractal coding is proposed. Fractal singular value neighbor distance is brought forward based on fractal neighbor distance. Fractal coding and local singular value decomposition are used to improve the recognition rate. The experimental results show that the method can keep a good robustness to the variation of illumination, pose and expression, compared with fractal neighbor distances. Furthermore, the method shows that the training time is short and recognition rate is high.
Keywords:Face recognition  Fractal coding  Local singular value decomposition  Fractal singular value neighbor distance
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