基于奇异值分解的人脸识别方法

梁毅雄; 龚卫国; 潘英俊; 李伟红; 刘嘉敏; 张红梅

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基于奇异值分解的人脸识别方法

信息科学 | 更新时间：2020-08-12

- 基于奇异值分解的人脸识别方法
- Singular value decomposition-based approach for face recognition
- 光学精密工程 2004年12卷第5期页码：543-549
- 作者机构：
  
  重庆大学, 光电技术及系统教育部重点实验室重庆,400044
- 作者简介：
- 基金信息：
  
  国家教育部科学技术重点项目(No.02057)();教育部春晖计划(No.2003589)的资助()
- DOI：
  中图分类号： TP391.4
- 收稿日期：2004-06-22，
  
  修回日期：2004-08-15，
  
  网络出版日期：2004-10-15，
  
  纸质出版日期：2004-10-15
- 稿件说明：
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梁毅雄, 龚卫国, 潘英俊, 李伟红, 刘嘉敏, 张红梅. 基于奇异值分解的人脸识别方法[J]. 光学精密工程, 2004,(5): 543-549

LIANG Yi-xiong, GONG Wei-guo, PAN Ying-jun, LI Wei-hong, LIU Jia-min, ZHANG Hong-mei . Singular value decomposition-based approach for face recognition[J]. Editorial Office of Optics and Precision Engineering, 2004,(5): 543-549
梁毅雄, 龚卫国, 潘英俊, 李伟红, 刘嘉敏, 张红梅. 基于奇异值分解的人脸识别方法[J]. 光学精密工程, 2004,(5): 543-549 DOI：

LIANG Yi-xiong, GONG Wei-guo, PAN Ying-jun, LI Wei-hong, LIU Jia-min, ZHANG Hong-mei . Singular value decomposition-based approach for face recognition[J]. Editorial Office of Optics and Precision Engineering, 2004,(5): 543-549 DOI：

摘要

提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换

得到其具有位移不变特性的振幅谱表征.其次

从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解

求出标准特征矩阵

再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后

对经典的最近邻分类器算法进行了改进

并采用模式特征之间的欧式距离作为相似性度量

从而完成对未知人脸的识别.采用ORL(Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证

获得了100.00%的识别率.实验结果表明

本方法优于现有的基于奇异值分解的人脸识别方法

且对表情、姿态变换等具有一定的鲁棒性.

Abstract

A method to extract algebraic features of a face image based on the Fourier transform and Singular Value Decomposition(SVD)is introduced

then the method with the algebraic feature is proposed to recognize faces. First

face images are processed by a 2D Fourier transform that has some effective properties such as a linear transform

and is invariant against spatial translation. The amplitudes of the transform coefficients are used to represent the image in the frequency domain. Second

the amplitude representation of the face image is projected onto the two compressed orthogonal matrixes

which come from the SVD of the standard face image obtained by averaging all training samples and then the projecting coefficients are used as the algebraic feature of the face image. The robustness of this feature is proved and used for face recognition. In the matching stage

the traditional Nearest Neighbor Classifier(NNC)is improved to recognize the unknown faces by using Euclidean distance as the similarity measurement. Finally

the standard face database from Olivetti Research Laboratory(ORL)is selected to evaluate the recognition accuracy of the proposed face recognition algorithm. This database includes face images with different expressions

small occlusions

different illumination conditions and different poses

etc. The recognition accuracy is up to 100.00% by selecting appropriate values of the parameters. The effectiveness of the proposed face recognition algorithm and its insensitivity to the facial expression

illumination and posture are shown in terms of both the absolute performance indices and the comparative performance against some popular face recognition schemes such as Singular Value decomposition-based method.

关键词

Keywords

references

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