Expression recognition in videos using a weighted component-based feature descriptor

Authors:
Xiaohua Huang;Guoying Zhao;Matti Pietikäinen;Wenming Zheng
Affiliations:
Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland and Research Center for Learning Science, Southeast University, China;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Research Center for Learning Science, Southeast University, China
Venue:
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Year:
2011

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Abstract

In this paper, we propose a weighted component-based feature descriptor for expression recognition in video sequences. Firstly, we extract the texture features and structural shape features in three facial regions: mouth, cheeks and eyes of each face image. Then, we combine these extracted feature sets using confidence level strategy. Noting that for different facial components, the contributions to the expression recognition are different, we propose a method for automatically learning different weights to components via the multiple kernel learning. Experimental results on the Extended Cohn-Kanade database show that our approach combining component-based spatiotemporal features descriptor and weight learning strategy achieves better recognition performance than the state of the art methods.