Tong Zhang;Yelin Chen;Shuzhen Li;Xiping Hu;C. L. Philip Chen
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引用次数: 0
Abstract
The human gait reflects substantial information about individual emotions. Current gait emotion recognition methods focus on capturing gait topology information and ignore the importance of fine-grained temporal features. This article proposes the temporal-tightly graph convolutional network (TT-GCN) to extract temporal features. TT-GCN comprises three significant mechanisms: the causal temporal convolution network (casual-TCN), the walking direction recognition auxiliary task, and the feature mapping layer. To obtain tight temporal dependencies and enhance the relevance among gait periods, the causal-TCN is introduced. Based on the assumption of emotional consistency in the walking directions, the auxiliary task is proposed to enhance the ability of fine-grained feature extraction. Through the feature mapping layer, affective features can be mapped into the appropriate representation and fused with deep learning features. TT-GCN shows the best performance across five comprehensive metrics. All experimental results verify the necessity and feasibility of exploring fine-grained temporal feature extraction.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.