|Table of Contents|

A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2016年04期
Page:
0-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition
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)
Keywords:
face recognition; samples fusion; kernel-induced; sparse
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2016.04.007
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.

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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Last Update: 2016-12-31