Computer Science ›› 2018, Vol. 45 ›› Issue (6): 275-283.doi: 10.11896/j.issn.1002-137X.2018.06.049

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Gabor Occlusion Dictionary Learning via Singular Value Decomposition

LI Xiao-xin1, ZHOU Yuan-shen1, ZHOU Xuan2, LI Jing-jing1, LIU Zhi-yong2   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310032,China1;
    Industry Center,Shenzhen Polytechnic,Shenzhen 518055,China2
  • Received:2017-03-24 Online:2018-06-15 Published:2018-07-24

Abstract: Covariate shift incurred by occlusion and illumination variations is an important problem for real-world face recognition systems.This paper explored this problem from the perspective of dictionary coding.By reviewing several extant structured error coding methods,this paper indicated that these error coding methods can be rewritten as a linear system by combining training dictionary and well-designed occlusion dictionary.Due to the importance of occlusion dictionary in structured error coding,this paper studied the dictionary learning method,K-SVD (Singular Value Decomposition),which is used in the Gabor feature based robust representation and classification (GRRC) method,and has been paid great attentions in the field of error coding.The K-SVD learned occlusion dictionary is strongly redundant and lack of natural structures.In addition,K-SVD is time-consuming.This paper proposed an SVD-based occlusion dictionary learning method.It is simple,but generates a more compacted and structured occlusion dictionary.Experiments on three face datasets,including Extended Yale B,UMBDB and AR,demonstrates that the proposed SVD-based GRRC consis-tently outperforms the K-SVD-based GRRC in several challenging situations.

Key words: Gabor feature, K-SVD, Occlusion dictionary, PCA, Singular value decomposition

CLC Number: 

  • TP391.4
[1]PARKHI O M,VEDALDI A,ZISSERMAN A.Deep Face Recognition [C]//Proceedings of British Machine Vision Confe-rence.London:BMVA Press,2015:41.
[2]SUN Y,WANG X,TANG X.Sparsifying Neural Network Connections for Face Recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:4856-4864.
[3]IOFFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift [C]//International Conference on Machine Learning.2015:448-456.
[4]ZHOU Z,WAGNER A,MOBAHI H,et al.Face Recognition with Contiguous Occlusion Using Markov Random Fields [C]//IEEE International Conference on Computer Vision.2009:1050-1057.
[5]HE R,ZHENG W,HU B.Maximum Correntropy Criterion for Robust Face Recognition [J].IEEE Transactions on PatternAnalysis and Machine Intelligence,2011,33(8):1561-1576.
[6]YANG M,ZHANG L,YANG J,et al.Regularized Robust Co-ding for Face Recognition [J].IEEE Transactions on Image Processing,2013,22(5):1753-1766.
[7]LI X,DAI D,ZHANG X,et al.Structured Sparse Error Coding for Face Recognition with Occlusion [J].IEEE Transactions on Image Processing,2013,22(5):1889-1900.
[8]YANG M,ZHANG L,SHIU S C,et al.Gabor Feature Based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary [J].Pattern Recognition,2013,46(7):1865-1878.
[9]YANG M,ZHANG L.Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary [C]//European Conference on Computer Vision.Springer Berlin Heidelberg,2010:448-461.
[10]DENG W,HU J,GUO J.Extended SRC:Undersampled Face Recognition via Intra-Class Variant Dictionary [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870.
[11]WEI X,LI C,HU Y.Robust Face Recognition under Varying Illumination and Occlusion Considering Structured Sparsity [C]//International Conference on Digital Image Computing Techniques and Applications.2012:1-7.
[12]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.
[13]JIA K,CHAN T H,MA Y.Robust and Practical Face Recognition via Structured Sparsity [C]//European Conference on Computer Vision.2012:331-344.
[14]YANG J,LUO L,QIAN J,et al.Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(1):156-171.
[15]HE R,ZHENG W S,TAN T,et al.Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(2):261-275.
[16]OU W,YOU X,TAO D,et al.Robust Face Recognition via Occlusion Dictionary Learning [J].Pattern Recognition,2014,47(4):1559-1572.
[17]AZEEM A,SHARIF M,RAZA M,et al.A Survey:Face Recognition Techniques under Partial Occlusion[J].International ArabJournal of Information Technology,2014,11(1):1-10.
[18]HASSABALLAH M,ALY S.Face Recognition:Challenges,Achievements and Future Directions [J].IET Computer Vision,2015,9(4):614-626.
[19]YANG M,FENG Z,SHIU S C K,et al.Fast and Robust Face Recognition via Coding Residual Map Learning Based Adaptive Masking [J].Pattern Recognition,2014,47(2):535-543.
[20]QIAN J,LUO L,YANG J,et al.Robust Nuclear Norm Regularized Regression for Face Recognition with Occlusion [J].Pattern Recognition,2015,48(10):3145-3159.
[21]WEN Y,LIU W,YANG M,et al.Structured Occlusion Coding for Robust Face Recognition [J].Neurocomputing,2016,178:11-24.
[22]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation [J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[23]HASTIE T,TIBSHIRANI R,FRIEDMAN J,et al.The Elements of Statistical Learning:Data Mining,Inference and Prediction [M].NewYork,USA:Springer,2005:83-85.
[24]TURK M,PENTLAND A.Eigenfaces for Recognition [J].Journal of Cognitive Neuroscience,1991,3(1):71-86.
[25]KIM W,SUH S,HWANG W,et al.SVD Face:Illumination-Invariant Face Representation [J].IEEE Signal Processing Letters,2014,21(11):1336-1340.
[26]CHAN T,JIA K,GAO S,et al.PCANet:A Simple Deep Learning Baseline for Image Classification? [J].IEEE Transactions on Image Processing,2015,24(12):5017-5032.
[27]ELHAMIFAR E,VIDAL R.Robust Classification Using Structured Sparse Representation [C]//IEEE Conference on Computer Vision and Pattern Recognition.2011:1873-1879.
[28]LEE K C,HO J,KRIEGMAN D J.Acquiring Linear Subspaces for Face Recognition under Variable Lighting [J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2005,27(5):684-698.
[29]GEORGHIADES A S,BELHUMEUR P N,KRIEGMAN D J.From Few to Many:Illumination Cone Models for Face Recognition under Variable Lighting and Pose [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):643-660.
[30]COLOMBO A,CUSANO C,SCHETTINI R.UMB-DB:A Database of Partially Occluded 3D Faces [C]//IEEE International Conference on Computer Vision Workshops.2011:2113-2119.
[31]MARTINEZ A M.The AR Face Database[D].Columbus,USA:Ohio State University,1998.
[32]EKENEL H,STIEFELHAGEN R.Why is Facial Occlusion a Challenging Problem? [C]//Proceedings of Advances in Biometrics.Springer Berlin,2009:299-308.
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