Facial expression recognition based on adaptive weighted fusion histograms

Authors:
Min Hu;Yanxia Xu;Liangfeng Xu;Xiaohua Wang
Affiliations:
College of Computer and Information, Hefei University of Technology, China,Affective Computing and Advanced Intelligent Machines AnHui Key Laboratory, Hefei, China;College of Computer and Information, Hefei University of Technology, China,Affective Computing and Advanced Intelligent Machines AnHui Key Laboratory, Hefei, China;College of Computer and Information, Hefei University of Technology, China;College of Computer and Information, Hefei University of Technology, China,Affective Computing and Advanced Intelligent Machines AnHui Key Laboratory, Hefei, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

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Abstract

In order to improve the performance of expression recognition, this paper proposes a facial expression recognition method based on adaptive weighted fusion histograms. Firstly, the method obtains expression sub-regions by pretreatment, and calculates the contribution maps (CM) of each expression sub-region. Secondly, this method extracts Histograms of Oriented Gradient by the Kirsch operator and extracts histograms of intensity by centralized binary pattern (CBP), then the paper fuses the Histograms by parallel manner and the fused histograms are weighted by CMs. At last, the weighted fused histograms are used to classify by the Euclidean Distance and the nearest neighbor method. Experimental results which are obtained by applying the proposed algorithm and the Gabor wavelet, LBP, LBP+LPP, Local Gabor and AAM on JAFFE face expression dataset show that the proposed method achieves better performance for the face expression recognition.