Motion profiles for deception detection using visual cues

Authors:
Nicholas Michael;Mark Dilsizian;Dimitris Metaxas;Judee K. Burgoon
Affiliations:
Computational Biomedicine Imaging & Modelling Center, Rutgers The State University of New Jersey, Piscataway, NJ;Computational Biomedicine Imaging & Modelling Center, Rutgers The State University of New Jersey, Piscataway, NJ;Computational Biomedicine Imaging & Modelling Center, Rutgers The State University of New Jersey, Piscataway, NJ;Center for the Management of Information, The University of Arizona, Tucson, AZ
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Year:
2010

Citing 5
Cited 2

Blob Analysis of the Head and Hands: A Method for Deception Detection

HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Automatic Thermal Monitoring System (ATHEMOS) for Deception Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Training professionals to detect deception

ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Automatic extraction of deceptive behavioral cues from video

ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Tracking facial features using mixture of point distribution models

ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing

Invited talk: coupling deformable models and learning methods for nonverbal behavior analysis: applications to deception, multi-cultural studies and ASL

ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
A review of motion analysis methods for human Nonverbal Communication Computing

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a data-driven, unobtrusive and covert method for automatic deception detection in interrogation interviews from visual cues only. Using skin blob analysis together with Active Shape Modeling, we continuously track and analyze the motion of the hands and head as a subject is responding to interview questions, as well as their facial micro expressions, thus extracting motion profiles, which we aggregate over each interview response. Our novelty lies in the representation of the motion profile distribution for each response. In particular, we use a kernel density estimator with uniform bins in log feature space. This scheme allows the representation of relatively over-controlled and relatively agitated behaviors of interviewed subjects, thus aiding in the discrimination of truthful and deceptive responses.