[1]沈学华,詹永照,程显毅,等.基于样本融合的核稀疏人脸识别方法[J].南京师范大学学报(自然科学版),2016,39(04):0.[doi:10.3969/j.issn.1001-4616.2016.04.007]
　Shen Xuehua,Zhan Yongzhao,Cheng Xianyi,et al.A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition[J].Journal of Nanjing Normal University(Natural Science Edition),2016,39(04):0.[doi:10.3969/j.issn.1001-4616.2016.04.007]
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基于样本融合的核稀疏人脸识别方法()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第39卷
期数:
2016年04期
页码:
0
栏目:
·数学与计算机科学·
出版日期:
2016-12-30

文章信息/Info

Title:
A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition
文章编号:
1001-4616(2016)04-0031-07
作者:
沈学华1; 2; 詹永照2; 程显毅1; 丁卫平1
(1.南通大学计算机科学与技术学院,江苏 南通 226019)(2.江苏大学计算机科学与通信工程学院,江苏 镇江 212013)
Author(s):
Shen Xuehua1; 2; Zhan Yongzhao2; Cheng Xianyi1; Ding Weiping1
(1.School of Computer Science & Technology,Nantong University,Nantong 226019,China)(2.School of Computer Science & Communications Engineering,Jiangsu University,Zhenjiang 212013,China)
关键词:
人脸识别; 样本融合; 核诱导; 稀疏表示; N最近邻
Keywords:
face recognition; samples fusion; kernel-induced; sparse
分类号:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2016.04.007
文献标志码:
A
摘要:
针对基于小样本集人脸图像的识别能力低,计算复杂度高的问题,提出了一种基于样本融合的核稀疏表示方法(KSRMSF). 该方法首先通过在原始样本集中添加镜像训练样本和对称训练样本,扩大了原始样本集的规模,接着使用基于高斯核函数的算法从扩充后的训练样本集中挑选若干个最近邻训练样本,利用这组最近邻样本的线性组合表示待识别的测试样本,根据L2范式的结果对测试样本进行分类,通过修改最近邻样本数获得更高的分类精度. 实验结果表明该方法比同类识别算法有更好的识别效果.
Abstract:
To improve the recognition rate of face recognition method based on small sample set of face images and reduce the high computational complexity,a kernel sparse representation method based on samples fusion method(KSRMSF)is proposed. The proposed method first extends the training samples to form a new training set by adding some mirror virtual training samples and symmetrical ones,and uses a algorithm based on Gaussian Kernel Function to determine N nearest neighbors of the testing sample from the new training samples. Finally,the KSRMSF represents the testing sample as a linear combination of the determinated N nearest neighbors and performs the classification according to the L2 norm. Through the different values of N set,the classification is more accurate. Many experiments show that the KSRMSF can get a better classification result than the same type of algorithm.

参考文献/References:

[1] XU Y,YANG J Y,LU J F,et al. An efficient renovation on kernel fisher discriminant analysis and face recognition experiments[J]. Pattern recognition,2004,37(10):2 091-2 094.
[2]KROEKER K L. Face recognition breakthrough[J]. Communications of the ACM,2009,52(8):18-19.
[3]TURK M,PENTLAND A. Eigenfaces for recognition[J]. Cognit Neurosci,1991,3(1):71-86.
[4]BELHUMEUR P N,HESPANHA J P,KRIENGMAN D J. Eigenfaces versus fisherfaces:recognition using class specific linear projection[J]. IEEE Trans Pattern Anal Mach Intell,1997,19(7):711-720
[5]TANG B,LUO S,HUANG H. High performance face recognition system by creating virtual sample[C]//Proceedings of International Conferenceon Neural Networks and Signal Processing,Toulouse,2003:972-975.
[6]COVER T,HART P. Nearest neighbor pattern classification[J]. IEEE transactions on information,1967,13(1):21-27.
[7]XU Y,ZHU Q,FAN Z,et al. Using the idea of the sparse representation to perform coarse-to-fine face recognition[J]. Information sciences,2013,238(7):138-148.
[8]XU T,ZHU N B. Two-phase method based on virtual test samples and face recognition experiments[C]//2015 12th International Conference on Fuzzy Systems and Knowledge Discovery(FSKD),Zhangjiajie,2015:1 253-1 257.
[9]XU Y,LI X L,YANG J,et al. Integrate the original face image and its mirror image for face recognition[J]. Neurocomputing,2014,131(7):191-199.
[10]XU Y,ZHANG D,YANG J,et al. A two-phase test sample sparse representation method for use with face recognition[J]. IEEE transactions on circuits and systems for video technology,2011,21(9):1 255-1 262.
[11]XU Y,ZHU Q. A simple and fast representation-based face recognition method[J]. Neural computing and applications,2013,22(7/8):1 543-1 549.
[12]TANG D Y,ZHU N B,YU F,et al. A novel sparse representation method based on virtual samples for face recognition[J]. Neural computing and applications,2014,24(3):513-519.
[13]XU Y,ZHU X J,LIU Z M. Using the original and“symmetrical face”training samples to perform representation based two-step face recognition[J]. Pattern recognition,2013,46(4):1 151-1 158.

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备注/Memo

备注/Memo:
收稿日期:2016-08-16.
基金项目:国家自然科学基金(61170126、61340037、61300167、61402205)、江苏省普通高校研究生科研创新计划资助项目(CXLX13_67)、南通市科技计划应用研究资助项目(BK2012038).
通讯联系人:沈学华,博士研究生,讲师,研究方向:图像识别、机器学习. E-mail:sxh003@ntu.edu.cn
更新日期/Last Update: 2016-12-31