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Person identity recognition on motion capture data using multiple actions

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Abstract

In this paper, we introduce a novel method for person identity recognition (identification) on skeleton animation/motion capture data representing persons performing various actions. The joints positions or orientation angles and the forward differences of these quantities are used to represent a motion capture sequence. First K-means clustering is applied on training data to discover the most representative patterns on joints positions or orientation angles (dynemes) and their forward differences (F-dynemes). Each frame is then assigned to one of these patterns and the frequency of occurrence histograms for each movement are constructed in a bag-of-words fashion. Person identity recognition is done through a nearest neighbor classifier. The proposed method is experimentally tested on a number of datasets of motion capture data, with very good results.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 541 24, Thessaloníki, Greece

    Ioannis Kapsouras & Nikos Nikolaidis

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  1. Ioannis Kapsouras
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  2. Nikos Nikolaidis
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Correspondence to Ioannis Kapsouras.

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Kapsouras, I., Nikolaidis, N. Person identity recognition on motion capture data using multiple actions. Machine Vision and Applications 26, 905–918 (2015). https://doi.org/10.1007/s00138-015-0704-z

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  • DOI: https://doi.org/10.1007/s00138-015-0704-z

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