Computer Science ›› 2015, Vol. 42 ›› Issue (8): 48-51, 85.

Previous Articles     Next Articles

Improved LSRC and its Application in Face Recognition

YIN He-feng, WU Xiao-jun and CHEN Su-gen   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Recently,sparse representation based classification(SRC) has attracted much attention in face recognition tasks.SRC forms the dictionary by directly using all the training samples.When giving lots of training samples,the speed of the subsequent sparse solver can be very slow.To alleviate this problem,a new local SRC,which is based on the similarities of sparse coefficients of both training samples and test samples,was presented.According to this similarity,a certain number of training samples are selected to form the over-complete dictionary,and then the test sample is decomposed using this dictionary.In contrast to original LSRC,which is based on kNN to choose neighbors of test samples,the proposed approach can steadily achieve better performance.Experimental results obtained on the ORL database,Yale database and AR database indicate that the proposed method is superior to both SRC and LSRC.

Key words: Sparse representation based classification(SRC),Local SRC(LSRC),Sparse coefficients,Similarity,Face recognition

[1] Turk M,Pentland A.Eigenfaces for recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71-86
[2] Belhumeur P N,Hespanha J P,Kriegman D.Eigenfaces vs.fisherfaces:Recognition using class specific linear projection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720
[3] He Xiao-fei,Yan Shui-cheng,Hu Yu-xiao,et al.Face recognition using Laplacianfaces[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340
[4] Wright J,Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227
[5] Wei Chia-po,Chao Yu-wei,Yeh Yi-ren,et al.Locality-sensitive dictionary learning for sparse representation based classification[J].Pattern Recognition,2013,46(5):1277-1287
[6] Liu Hui-dong,Yang Ming,Gao Yang,et al.Bilinear discriminative dictionary learning for face recognition[J].Pattern Recognition,2014,47(5):1835-1845
[7] Yang Meng,Zhang Lei,Feng Xiang-chu,et al.Fisher discrimination dictionary learning for sparse representation[C]∥IEEE International Conference on Computer Vision.2011:543-550
[8] Jiang Zhuo-lin,Lin Zhe,Davis Larry S.Label consistent K-SVD:learning a discriminative dictionary for recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2651-2664
[9] Xu Yong,Zhang D,Yang Jian,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):1255-1262
[10] Zhang Lei,Yang Meng,Feng Xiang-chu.Sparse representation or collaborative representation:Which helps face recognition?[C]∥IEEE International Conference on Computer Vision(ICCV).2011:471-478
[11] Mi Jian-Xun.Face image recognition via collaborative representation on selected training samples[J].Optik-International Journal for Light and Electron Optics,2013,124(18):3310-3313
[12] Feng Zhi-zhao,Yang Meng,Zhang Lei,et al.Joint discriminative dimensionality reduction and dictionary learning for face recognition[J].Pattern Recognition,2013,46(8):2134-2143
[13] Liu Bao-di,Wang Yu-xiong,Zhang Yu-jin,et al.Learning dictionary on manifolds for image classification[J].Pattern Recognition,2013,46(7):1879-1890
[14] Li Chun-guang,Guo Jun,Zhang Hong-gang.Local sparse representation based classification[C]∥International Conference on Pattern Recognition.2010:649-652
[15] Hotta S,Kiyasu S,Miyahara S.Pattern recognition using ave-rage patterns of categorical k-nearest neighbors[C]∥Procee-dings of the 17th International Conference on Pattern Recognition.2004,4:412-415
[16] Cheng Bin,Yang Jian-chao,Yan Shui-cheng,et al.Learningwith-graph for image analysis[J].IEEE Transactions on Image Processing,2010,19(4):858-866
[17] Martinez A M,Benavente R.The AR face database.CVC Technical Report[R].1998,24
[18] Kim S J,Koh K,Lustig M,et al.A method for large-scale-regularized least squares[J].IEEE Journal on Selected Topics in Signal Processing,2007,1(4):606-617

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .