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. © 2010 Springer-Verlag.
CITATION STYLE
Michael, N., Dilsizian, M., Metaxas, D., & Burgoon, J. K. (2010). Motion profiles for deception detection using visual cues. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6316 LNCS, pp. 462–475). Springer Verlag. https://doi.org/10.1007/978-3-642-15567-3_34
Mendeley helps you to discover research relevant for your work.