Computer Science ›› 2015, Vol. 42 ›› Issue (Z6): 180-183.

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Face Recognition Based on Matrix Regression with Low-rank and p Sparse Constraints

YANG Guo-liang, LUO Lu, LU Hai-rong, FENG Yi-qin and LIANG Li-ming   

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

Abstract: This paper presented a model of matrix regression for face recognition to deal with varying illumination,as well as occlusion and disguise.To ensure low rank and sparse prosperities of the model,we used low rankness to constraint the regression error,and used the p-norm to constraint the regression coefficients in order to guarantee the sparest solution.We applied generalized iterated shrinkage algorithm for p-norm,and alternating direction method for regression coefficients.Experiment results on face database of AR and Extended Yale B show that the face recognition method proposed in this paper has a higher recognition rate than the current regression methods.And our method is more powerful for removing the structural noise caused by occlusion,and more robust for alleviating the effect of illumination.

Key words: Face recognition,Nuclear norm,p-norm,Generalized iterated shrinkage algorithm,Robust regression,Alternating direction method of multipliers

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