Recognizing affect from non-stylized body motion using shape of Gaussian descriptors

Authors:
Liyu Gong;Tianjiang Wang;Chengshuo Wang;Fang Liu;Fuqiang Zhang;Xiaoyuan Yu
Affiliations:
Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China;Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China;Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China;Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China;Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China;Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
Venue:
Proceedings of the 2010 ACM Symposium on Applied Computing
Year:
2010

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Abstract

In this paper, we address the problem of recognizing affect from non-stylized human body motion. We utilize a novel feature descriptor which is based on the shape of signal probability density function framework to represent the motion capture data. Combining the feature representation scheme with support vector machine classifier, we detect implicitly communicated affect in human body motion. We test our algorithm using a comprehensive database of affectively performed motion. Experiment results show state-of-the-art performance compared with the existing methods.