An Occluded Facial Expression Recognition Method Based on Sparse Representation
ZHU Ming-Han1,2, LI Shu-Tao1, YE Hua2
1College of Electrical and Information Engineering, Hunan University, Shangsha 410082 2College of Electrical and Information Engineering, Hunan University of Arts and Science, Changde 415000
Abstract Occlusion dictionary does not have redundancy and facial expression classification is easily disturbed by identity features, which sparse representation based classification(SRC) is used to recognize occluded facial expression. A method for occluded facial expression recognition is proposed to solve this problem. Firstly, an occlusion dictionary with redundancy is constructed by multilevel blocking of the image. Next, sparse representation coefficients of the test image are gained by spare decomposition. Finally, the expression category of test image is judged in its individual subspace. The proposed method makes decomposition coefficients of the test image sparser and avoids identity feature interference to expression classification. The experimental results on Cohn-Kanade and JAFFE face databases show that the proposed method is robust to occluded facial expression recognition.
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2014, Vol. 27 
