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Facial expression recognition under partial occlusion based on fusion of global and local features
Author(s): Xiaohua Wang; Chen Xia; Min Hu; Fuji Ren
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Paper 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.

Paper Details

Date Published: 10 April 2018
PDF: 12 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150M (10 April 2018); doi: 10.1117/12.2303417
Show Author Affiliations
Xiaohua Wang, Huaiyin Institute of Technology (China)
Hefei Univ. of Technology (China)
Chen Xia, Hefei Univ. of Technology (China)
Min Hu, Hefei Univ. of Technology (China)
Fuji Ren, Hefei Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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