Body Motion Analysis for Emotion Recognition in Serious Games

Abstract:


In this paper, we present an emotion recognition methodology that utilizes information extracted from body motion analysis to assess affective state during gameplay scenarios. A set of kinematic and geometrical features are extracted from joint-oriented skeleton tracking and are fed to a deep learn-ing network classifier. In order to evaluate the performance of our methodolo-gy, we created a dataset with Microsoft Kinect recordings of body motions ex-pressing the five basic emotions (anger, happiness, fear, sadness and surprise) which are likely to appear in a gameplay scenario. In this five emotions recog-nition problem, our methodology outperformed all other classifiers, achieving an overall recognition rate of 93%. Furthermore, we conducted a second series of experiments to perform a qualitative analysis of the features and assess the descriptive power of different groups of features.


  • K. Kaza , A. Psaltis , K. Stefanidis , K. Apostolakis , S. Thermos , K. Dimitropoulos, P. Daras, "Body Motion Analysis for Emotion Recognition in Serious Games", HCI International 2016, Toronto, Canada, 17 - 22 July 2016.

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    Contact Information

    Dr. Petros Daras, Principal Researcher Grade Α
    1st km Thermi – Panorama, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras@iti.gr