[1]李晓东,费树岷,张涛.基于奇异值特征和支持向量机的人脸识别[J].东南大学学报(自然科学版),2008,38(6):981-985.[doi:10.3969/j.issn.1001-0505.2008.06.009]
 Li Xiaodong,Fei Shumin,Zhang Tao.Face recognition based on singular value feature and support vector machines[J].Journal of Southeast University (Natural Science Edition),2008,38(6):981-985.[doi:10.3969/j.issn.1001-0505.2008.06.009]
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基于奇异值特征和支持向量机的人脸识别()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
38
期数:
2008年第6期
页码:
981-985
栏目:
计算机科学与工程
出版日期:
2008-11-20

文章信息/Info

Title:
Face recognition based on singular value feature and support vector machines
作者:
李晓东 费树岷 张涛
东南大学复杂工程系统测量与控制教育部重点实验室, 南京 210096; 东南大学自动化学院, 南京 210096
Author(s):
Li Xiaodong Fei Shumin Zhang Tao
Key Laboratory of Measurement and Control of CSE of Ministry of Education,Southeast University,Nanjing 210096,China
School of Automation,Southeast University,Nanjing 210096,China
关键词:
奇异值特征 支持向量机 人脸识别
Keywords:
singular value feature support vector machines face recognition
分类号:
TP391.41
DOI:
10.3969/j.issn.1001-0505.2008.06.009
摘要:
针对人脸识别中经常遇到的“小样本”和“过学习”等问题,同时为了进一步改善人脸图像的奇异值特征在人脸识别中的识别性能,提出了一种基于奇异值分解和支持向量机的人脸识别新方法.在特征提取阶段,首先对训练样本集中的每一个人脸图像矩阵进行奇异值分解,得到训练样本的奇异值特征,然后对每个样本的奇异值特征向量进行降维、归一化、奇异值向量的分量重新排列等处理.在识别阶段,运用支持向量机作为分类工具,为了提高分类能力,选取径向基函数作为支持向量机的核函数.最后在ORL人脸数据库上验证了该方法.实验结果表明,通过对奇异值特征的相关处理,提高了识别速度和正确识别率.从而证明了所提出方法的有效性,具有一定的应用价值.
Abstract:
A new approach for face recognition based on singular value feature and support vector machine is presented to improve the recognition performance of singular value feature vector. At the same time, this method can be applied to solve both small sample problem and overfitting problem. Firstly, singular value decomposing is performed on every facial image of training set to get singular value features of training samples. Subsequently, several steps including dimension reduction, normalizing,and rearranging the elements order of every feature vector and so on are conducted over all the singular value feature vectors. Finally, support vector machine is used as classifier, and the RBF(radial basis of functions)function is adopted to be the kernel function to increase the classifying ability. Experiment results on ORL(olivetti research laboratory)database demonstrate that the approach proposed in this paper is efficient, and has some application values.

参考文献/References:

[1] Zhao W,Chellappa R,Phillips P J,et al.Face recogni-tion:a literature survey[J].Acm Computing Surveys,2003,35(4):399-459.
[2] 刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法[J].自动化学报,2003,29(6):900-911.
  Liu Qingshan,Lu Hanqing,Ma Songde.A survey:subspace analysis for face recognition[J]. Acta Automatica Sinica,2003,29(6):900-911.(in Chinese)
[3] Hong Z.Algebraic feature extraction of image for recognition [J].Pattern Recognition,1991,24(3):211-219.
[4] 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 Yunhong,Tan Tieniu,Zhu Yong.Face identification based on singular value decomposition and data fusion[J].Chinese J Computers,2000,23(6):649-653.(in Chinese)
[5] 甘俊英,张有为.一种基于奇异值特征的神经网络人脸识别新途径[J].电子学报,2004,32(1):170-173.
  Gan Junying,Zhang Youwei.A new approach or face recognition based on singular value features and neural networks[J].Acta Electronica Sinica,2004,32(1):170-173.(in Chinese)
[6] Klema V C,Laub A J.Singular value decomposition:its computation and some applications[J].IEEE Transactions on Automatic Control,1980,25(2):164-176.
[7] Cortes C,Vapnik V.Support-vector network[J].Machine Learning,1995,20(3):273-297.
[8] 张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.
  Zhang Xuegong.Introduction to statistical learning theory and support vector machines[J].Acta Automatica Sinica,2000,26(1):32-42.(in Chinese)
[9] Mayoraz E,Alpaydin E.Support vector machines for multi-class classification[C] //Proceedings of International Work-Conference on Artificial and Natural Neural Networks.Berlin,Germany,1999,2:833-842.
[10] Hsu C W,Lin C J.A simple decomposition method for support vector machines[J]. Machine Learning,2002,46(1):291-314.
[11] Hsu C W,Lin C J.A comparison of methods for multiclass support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425.
[12] Chang Chih-chung,Lin Chin-Jen.LIBSVM:a library for support vector machines[EB/OL].(2001)[2008-11].http://www.csie.ntu.edu.tw/~cjlin/libsvm.

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备注/Memo

备注/Memo:
作者简介: 李晓东(1974—),男,博士生; 费树岷(联系人),男,博士,教授,博士生导师,smfei@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60574006).
引文格式: 李晓东,费树岷,张涛.基于奇异值特征和支持向量机的人脸识别[J].东南大学学报:自然科学版,2008,38(6):981-985
更新日期/Last Update: 2008-11-20