TY - GEN
T1 - Hmm-based deception recognition from visual cues
AU - Tsechpenakis, Gabriel
AU - Metaxas, Dimitris
AU - Adkins, Mark
AU - Kruse, John
AU - Burgoon, Judee K.
AU - Jensen, Matthew L.
AU - Meservy, Thomas
AU - Twitchell, Douglas P.
AU - Deokar, Amit
AU - Nunamaker, Jay F.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33750547359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750547359&partnerID=8YFLogxK
U2 - 10.1109/ICME.2005.1521550
DO - 10.1109/ICME.2005.1521550
M3 - Conference contribution
AN - SCOPUS:33750547359
SN - 0780393325
SN - 9780780393325
T3 - IEEE International Conference on Multimedia and Expo, ICME 2005
SP - 824
EP - 827
BT - IEEE International Conference on Multimedia and Expo, ICME 2005
T2 - IEEE International Conference on Multimedia and Expo, ICME 2005
Y2 - 6 July 2005 through 8 July 2005
ER -