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Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior
September/October 2005 (vol. 20 no. 5)
pp. 36-43
ASCII Text
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Thomas O. Meservy, Matthew L. Jensen, John Kruse, Douglas P. Twitchell, Gabriel Tsechpenakis, Judee K. Burgoon, Dimitris N. Metaxas, Jay F. Nunamaker Jr., "Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior," IEEE Intelligent Systems, vol. 20, no. 5, pp. 36-43, September/October, 2005.
 
 
BibTex
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@article{ 10.1109/MIS.2005.85,
author = {Thomas O. Meservy and Matthew L. Jensen and John Kruse and Douglas P. Twitchell and Gabriel Tsechpenakis and Judee K. Burgoon and Dimitris N. Metaxas and Jay F. Nunamaker Jr.},
title = {Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior},
journal ={IEEE Intelligent Systems},
volume = {20},
number = {5},
issn = {1541-1672},
year = {2005},
pages = {36-43},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2005.85},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior
IS - 5
SN - 1541-1672
SP36
EP43
EPD - 36-43
A1 - Thomas O. Meservy,
A1 - Matthew L. Jensen,
A1 - John Kruse,
A1 - Douglas P. Twitchell,
A1 - Gabriel Tsechpenakis,
A1 - Judee K. Burgoon,
A1 - Dimitris N. Metaxas,
A1 - Jay F. Nunamaker Jr.,
PY - 2005
KW - face and gesture recognition
KW - video analysis
KW - feature representation
KW - decision support
VL - 20
JA - IEEE Intelligent Systems
ER -
 
Thomas O. Meservy, University of Arizona
Matthew L. Jensen, University of Arizona
John Kruse, University of Arizona
Douglas P. Twitchell, Illinois State University
Gabriel Tsechpenakis, Rutgers University
Judee K. Burgoon, University of Arizona
Dimitris N. Metaxas, Rutgers University
Jay F. Nunamaker Jr., University of Arizona
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.

Index Terms:
face and gesture recognition, video analysis, feature representation, decision support
Citation:
Thomas O. Meservy, Matthew L. Jensen, John Kruse, Douglas P. Twitchell, Gabriel Tsechpenakis, Judee K. Burgoon, Dimitris N. Metaxas, Jay F. Nunamaker Jr., "Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior," IEEE Intelligent Systems, vol. 20, no. 5, pp. 36-43, Sep./Oct. 2005, doi:10.1109/MIS.2005.85
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