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Gesture-Based Affective Computing on Motion Capture Data

  • Asha Kapur
  • Ajay Kapur
  • Naznin Virji-Babul
  • George Tzanetakis
  • Peter F. Driessen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)

Abstract

This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Asha Kapur
    • 2
  • Ajay Kapur
    • 1
  • Naznin Virji-Babul
    • 1
  • George Tzanetakis
    • 1
  • Peter F. Driessen
    • 1
  1. 1.University of VictoriaVictoriaCanada
  2. 2.School of MedicineWake Forest UniversityNorth CarolinaUnited States

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