HMM-BASED DECEPTION RECOGNITION FROM VISUAL CUES (WedAmOR1)

Author(s) : Gabriel Tsechpenakis (Rutgers University, United States of America) Dimitris Metaxas (Rutgers University, United States of America) Mark Adkins (CMI, The University of Arizona, United States of America) John Kruse (CMI, The University of Arizona, United States of America) Matthew Jensen (CMI, The University of Arizona, United States of America) Thomas Meservy (CMI, The University of Arizona, United States of America) Douglas Twitchell (CMI, The University of Arizona, United States of America) Amit Deokar (CMI, The University of Arizona, United States of America) Jay Nunamaker (CMI, The University of Arizona, United States of America) Judee Burgoon (The University of Arizona, United States of America)

Abstract : Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment humans in detecting deception by analyzing nonverbal behavior on video. By tracking faces and hands of an individual, it is anticipated that objective behavioral indicators of deception can be isolated, extracted and synthesized to create a more accurate means for detecting human deception. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. A proof-of-concept study is presented that uses blob analysis to extract visual cues and events, throughout the examined videos. The integration of these cues is done using a hierarchical Hidden Markov Model to explore behavioral state identification in the detection of deception, mainly involving the detection of agitated and over-controlled behaviors.

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