Singular value decomposition-based approach for face recognition

LIANG Yi-xiong; GONG Wei-guo; PAN Ying-jun; LI Wei-hong; LIU Jia-min; ZHANG Hong-mei

doi:null

您当前的位置：

首页 >

文章列表页 >

Singular value decomposition-based approach for face recognition

更新时间：2020-08-12

- Singular value decomposition-based approach for face recognition
- Optics and Precision Engineering Vol. 12, Issue 5, Pages: 543-549(2004)
- 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：
- DOI：
  CLC： TP391.4
- Received：22 June 2004，
  
  Revised：15 August 2004，
  
  Published Online：15 October 2004，
  
  Published：15 October 2004
- 稿件说明：
移动端阅览
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

SAMAL A, IYENGAR P A.Automatic recognnition and analysis of human faces and facial expression:A survey[J] .Pattern Recognition,1992,25(1):65-77.

王沛,余松煜,袁晓兵.基于小波变换自适应盲水印算法[J] .光学精密工程,2002,10(3):247-252.WANG P,YU S Y,YUAN X B.Adaptive blind watermarking algorithm based on wavelet transfor[J] . Optics and Precision Engineering,2002,10(3):247-252.(in Chinese)

代少升,袁祥辉.提高DSP图像处理系统实时性的一种有效方法[J] .光学精密工程,2003,11(6):617-620.DAI SH SH,YUAN X H.Improvement of DSP image processing real-timeness[J] . Optics and Precision Engineering,2003,11(6):617-620.(in Chinese)

CHELLAPPA R, WILSON C L, SIROHEY S. Human and machine recognition of faces: A survey[J] .Proceedings of IEEE. 1995, 83(5): 705-740.

LIU C J, WECHSLER H. Evolutionary pursuit and its application to face recognition[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(6): 570-582.

GRUDIN M A.Compact multi-level representation of human faces for recognition [D] . Liverpool John Moores University, UK, 1997.

GRUDIN M A. On internal representations in face recognition systems [J] .Pattern Recognition, 2000, 33(7): 1161-1177.

张翠平, 苏光大. 人脸识别技术综述[J] . 中国图像图形学报, 2000, 5(11): 885-894.ZHANG C P, SU G D. Human face recognition: A survey[J] .Journal of Image and Graphics, 2000, 5(11): 885-894.(in Chinese)

HONG Z Q. Algebraic feature extraction of image recognition[J] .Pattern Recognition, 1991, 24(3): 211-219.

TURK M A, PENTLAND A P. Face recognition using eigenfaces[C] . IEEE Proceedings on Computer Vision and Patter Recognition, 1991: 586-591.

TURK M A, PENTLAND A P. Eigenfaces for recognition[J] .The Journal of Cognitive Neuroscience,1991,3(1): 71-79.

洪子泉,杨静宇. 用于图象识别的图象代数特征抽取[J] . 自动化学报, 1992, 18(2): 233-238.HONG Z Q, YANG J Y. Algebraic feature extraction of image for image recognition [J] .Acta Automatica Sinica, 1992, 18(2): 233-238.(in Chinese)

CHENG Y Q. Human face recognition method based on the statistical model of small sample size[J] .SPIE, 1991, 1607: 85-95.

洪子泉,杨静宇.基于奇异值特征和统计模型的人像识别算法[J] .计算机研究与发展, 1994, 31(3): 60-65.HONG Z Q, YANG J Y. Human face recognition method based on SVs feature and the statistical model[J] .Computer Research and Development, 1994, 31(3): 60-65.(in Chinese)

WANG Y H, TAN T N, ZHU Y. Face verification based on singular value decomposition and radial basis function neural network[C] .Proc. 4 th. Asian Conference on Computer Vision, 2000: 432-436.

TIAN Y, TAN T N, WANG Y H, et al. Do singular values contain adequate information for recognition[J] . Pattern Recognition, 2003, 36: 649-655.

王蕴红, 谭铁牛, 朱勇.基于奇异值分解和数据融合的脸像鉴别[J] . 计算机学报, 2000, 23(6): 649-653.WANG Y H, TAN T N, ZHU Y. Face identification based on singular values decomposition and data fusion[J] .Chinese J. Computers, 2000, 23(6): 649-653.(in Chinese)

LAI J H, YUEN P C, FENG G C. Face recognition using holistic Fourier invariant features[J] .Pattern Recognition,2001, 34(1): 95-109.

KLEMA V C, LAUB A J. The Singular value decomposition: Its computation and some applications[J] .IEEE Transactions on Automatic Control,1980, 25(2): 164-176.

TIAN Q, FAINMAN Y, GU Z H, et al. Comparison of statistical pattern recognition algorithms for hybrid processing, Part II: Eigenvector-based algorithms[J] .Journal of the Optical Society of America, 1988, 5(10): 1670-1672.

COVER T M, HART P E. Nearest neighbor pattern classification[J] .IEEE Transactions on Information Theory,1967, 13(1): 21-27.

Views

488

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Face recognition algorithm incorporating CBAM and Siamese neural network

Multi-regularization image restoration method for X-ray images of integrated circuit

Shortwave infrared visible-light face recognition based on content feature extraction

Measuring multi-surface shape by Fourier transform

Detection of periodic microstructure defect based on optical Fourier transform

Related Author

MENG Tiansheng

WANG Guicong

LI Yingjun

MENG Xiangzhou

ZOU Tao

ZHAO Zhijia

LI Zhifu

LIN Sen

Related Institution

School of Mechanical Engineering， University of Jinan

School of Mechanical and Electrical Engineering， Guangzhou University

University of Chinese Academy of Science

Key Laboratory of Infrared System Detection and Imaging Technology， Chinese Academy of Science

Shanghai Institute of Technical Physics， Chinese Academy of Science

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰