Lie to Me: Deceit detection via online behavioral learning

Inspired by the the behavioral scientific discoveries of Dr. Paul Ekman in relation to deceit detection, along with the television drama series Lie to Me, also based on Dr. Ekman's work, we use machine learning techniques to study the underlying phenomena expressed when a person tells a lie. We build an automated framework which detects deceit by measuring the deviation from normal behavior, at a critical point in the course of an investigative interrogation. Behavioral psychologists have shown that the eyes (via either gaze aversion or gaze extension) can be good reflectors of the inner emotions, when a person tells a high-stake lie. Hence we develop our deceit detection framework around eye movement changes. A dynamic bayesian model of eye movements is trained during a normal course of conversation for each subject, to represent normal behavior. The remaining conversation is broken into sequences and each sequence is tested against the parameters of the model of normal behavior. At the critical points in the interrogations, the deviations from normalcy are observed and used to deduce verity/deceit. An analysis on 40 subjects gave an accuracy of 82.5% which strongly suggests that the latent parameters of eye movements successfully capture behavioral changes and could be viable for use in automated deceit detection.