Statistical Person Verification Using Behavioral Patterns from Complex Human Motion

  • Felipe Gomez-Caballero
  • Takahiro Shinozaki
  • Sadaoki Furui
  • Koichi Shinoda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)

Abstract

We propose a person verification method based on behavioral patterns from complex human movements. Behavioral patterns are represented by anthropometric and kinematic features of human body motion acquired by a Kinect RGBD sensor. We focus on complex movements to demonstrate that independent and rhythmic movement of body parts carries a significant amount of behavioral information. We take a statistical approach by Gaussian mixture models to model the individual behavioral patterns. We demonstrate that subject-preferred movements are more robust against forgery attacks and variations over time than predetermined subject-independent movements. The obtained equal error rate was 15.7% when using subject-preferred movements and 27.3% when using a predefined sequence of movements.

Keywords

person verification individuality human movement GMM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Felipe Gomez-Caballero
    • 1
  • Takahiro Shinozaki
    • 1
  • Sadaoki Furui
    • 1
  • Koichi Shinoda
    • 1
  1. 1.Tokyo Institute of TechnologyTokyoJapan

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