TY - GEN
T1 - Perinasal indicators of deceptive behavior
AU - Dcosta, Malcolm
AU - Shastri, Dvijesh
AU - Vilalta, Ricardo
AU - Burgoon, Judee K.
AU - Pavlidis, Ioannis
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - High-stakes lying causes detectable changes in human behavior and physiology. Lie detection techniques based on behavior analysis are unobtrusive, but often require laborintensive efforts. Lie detection techniques based on physiological measurements are more amenable to automated analysis and perhaps more objective, but their often obtrusive nature makes them less suitable for realistic studies. In this paper we present a novel lie detection framework. At the core of this framework is a physiological measurement method that quantifies stress-induced facial perspiration via thermal imagery. The method uses a wavelet-based signal processing algorithm to construct a feature vector of dominant perinasal perspiration frequencies. Then, pattern recognition algorithms classify the subjects into deceptive or truthful by comparing the extracted features between the hard and easy questioning segments of an interview procedure. We tested the framework on thermal clips of 40 subjects who underwent interview for a mock crime. We used 25 subjects to train the classifiers and 15 subjects for testing. The method achieved 80% success rate in blind predictions. This framework can be generalized across experimental designs, as the classifiers do not depend on the number or order of interview questions.
AB - High-stakes lying causes detectable changes in human behavior and physiology. Lie detection techniques based on behavior analysis are unobtrusive, but often require laborintensive efforts. Lie detection techniques based on physiological measurements are more amenable to automated analysis and perhaps more objective, but their often obtrusive nature makes them less suitable for realistic studies. In this paper we present a novel lie detection framework. At the core of this framework is a physiological measurement method that quantifies stress-induced facial perspiration via thermal imagery. The method uses a wavelet-based signal processing algorithm to construct a feature vector of dominant perinasal perspiration frequencies. Then, pattern recognition algorithms classify the subjects into deceptive or truthful by comparing the extracted features between the hard and easy questioning segments of an interview procedure. We tested the framework on thermal clips of 40 subjects who underwent interview for a mock crime. We used 25 subjects to train the classifiers and 15 subjects for testing. The method achieved 80% success rate in blind predictions. This framework can be generalized across experimental designs, as the classifiers do not depend on the number or order of interview questions.
UR - http://www.scopus.com/inward/record.url?scp=84944916557&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944916557&partnerID=8YFLogxK
U2 - 10.1109/FG.2015.7163080
DO - 10.1109/FG.2015.7163080
M3 - Conference contribution
AN - SCOPUS:84944916557
T3 - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
BT - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
Y2 - 4 May 2015 through 8 May 2015
ER -