Remote Thermal Imaging for Reliable Deception Detection

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-04-M-0139
Agency Tracking Number: N041-144-1202
Amount: $69,829.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solitcitation Year: 2004
Solitcitation Topic Code: N04-144
Solitcitation Number: 2004.1
Small Business Information
BARRON ASSOC., INC.
1160 Pepsi Place, Suite 300, Charlottesville, VA, 22901
Duns: 120839477
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 B. Parker
 Sr. Research Scientist
 (434) 973-1215
 parker@barron-associates.com
Business Contact
 David Ward
Title: President
Phone: (434) 973-1215
Email: barron@barron-associates.com
Research Institution
N/A
Abstract
Thermal imaging of the face and head for the purpose of deception detection has shown significant promise in recent studies. This technique could be used to enhance polygraph examinations and, because it is noninvasive, could also potentially be used for mass screening at critical security checkpoints. The primary technical obstacles that must be overcome in order to realize a system suitable for these applications are: (1) achievement of adequate thermal resolution to detect the signals of interest, (2) localization of the appropriate target area at a distance, and (3) realization of automated signal processing software. Barron Associates, Inc. (BAI) has teamed with Mayo Clinic to propose a research program that will enhance the understanding of the physiology of deception and will produce a system targeted for employment in mass screening applications. The Mayo Clinic will perform clinical experiments using its system for thermal imaging of the face (STIF) to capture the transient thermal responses associated with deception. BAI will implement invariant feature extraction algorithms, apply these algorithms to the captured thermal image sequences, and use these invariant features as input to its GNOSIS neural network synthesis software to synthesize automated classification networks that can be used for real-time deception detection.

* information listed above is at the time of submission.

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