Exploiting multi-expression dependences for implicit multi-emotion video tagging |
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Authors: | Shangfei Wang Zhilei Liu Jun Wang Zhaoyu Wang Yongqiang Li Xiaoping Chen Qiang Ji |
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Affiliation: | 1. Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, PR China;2. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Hei Longjiang 15000, PR China;3. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA |
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Abstract: | In this paper, a novel approach of implicit multiple emotional video tagging is proposed, which considers the relations between the users' facial expressions and emotions as well as the relations among multiple expressions. First, the audiences' expressions are inferred through a multi-expression recognition model, which consists of an image-driven expression measurement recognition and a Bayesian network representing the co-existence and mutual exclusion relations among multi-expressions. Second, the videos' multi-emotion tags are obtained from the recognized expressions by another Bayesian Network, capturing the relations between expressions and emotions. Results of the experiments conducted on the JAFFE and NVIE databases demonstrate that the performance of expression recognition is improved by considering the relations among multiple expressions. Furthermore, the relations between expressions and emotions help improve emotional tagging, as our approach outperforms the traditional expression-based or image-driven implicit tagging methods. |
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Keywords: | Implicit video tagging Multi-emotion Multi-expression |
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