Motion Profiles for Deception Detection Using Visual Cues

  • Nicholas Michael
  • Mark Dilsizian
  • Dimitris Metaxas
  • Judee K. Burgoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)


We propose a data-driven, unobtrusive and covert method for automatic deception detection in interrogation interviews from visual cues only. Using skin blob analysis together with Active Shape Modeling, we continuously track and analyze the motion of the hands and head as a subject is responding to interview questions, as well as their facial micro expressions, thus extracting motion profiles, which we aggregate over each interview response. Our novelty lies in the representation of the motion profile distribution for each response. In particular, we use a kernel density estimator with uniform bins in log feature space. This scheme allows the representation of relatively over-controlled and relatively agitated behaviors of interviewed subjects, thus aiding in the discrimination of truthful and deceptive responses.


Nonverbal Behavior Interview Response Active Shape Model Multiple Instance Learn Deception Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nicholas Michael
    • 1
  • Mark Dilsizian
    • 1
  • Dimitris Metaxas
    • 1
  • Judee K. Burgoon
    • 2
  1. 1.Computational Biomedicine Imaging & Modelling Center (CBIM)Rutgers The State University of New JerseyPiscataway
  2. 2.Center for the Management of Information (CMI)The University of ArizonaTucson

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