Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior

Authors:
Thomas O. Meservy;Matthew L. Jensen;John Kruse;Douglas P. Twitchell;Gabriel Tsechpenakis;Judee K. Burgoon;Dimitris N. Metaxas;Jay F. Nunamaker Jr.
Affiliations:
University of Arizona;University of Arizona;University of Arizona;Illinois State University;Rutgers University;University of Arizona;Rutgers University;University of Arizona
Venue:
IEEE Intelligent Systems
Year:
2005

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Abstract

Accurately and consistently detecting deception is a daunting and persistent challenge for security personnel. Biases and human cognitive limitations make accurately and reliably detecting deception more difficult. An unobtrusive system for detecting deception from nonverbal behavioral cues extracts information about the movements of the hands and head and automatically identifies behavioral patterns that indicate deception. The system classifies deception and truth with greater accuracy than humans.This article is part of a special issue on homeland security.