Computer Science

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

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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
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