Facial expression recognition under partial occlusion based on fusion of global and local features

Xiaohua Wang; Chen Xia; Min Hu; Fuji Ren

doi:10.1117/12.2303417

10 April 2018 Facial expression recognition under partial occlusion based on fusion of global and local features

Xiaohua Wang, Chen Xia, Min Hu, Fuji Ren

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150M (2018) https://doi.org/10.1117/12.2303417
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

Citation Download Citation

Xiaohua Wang, Chen Xia, Min Hu, and Fuji Ren "Facial expression recognition under partial occlusion based on fusion of global and local features", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150M (10 April 2018); https://doi.org/10.1117/12.2303417

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