Motion profiles for deception detection using visual cues

Nicholas Michael, Mark Dilsizian, Dimitris Metaxas, Judee K. Burgoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
Number of pages14
EditionPART 6
ISBN (Print)3642155669, 9783642155666
StatePublished - 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: Sep 10 2010Sep 11 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 6
Volume6316 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th European Conference on Computer Vision, ECCV 2010
CityHeraklion, Crete


  • deception
  • face tracking
  • nearest-neighbor
  • skin blob tracking
  • statistical shape models
  • support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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