Detection of Deception in Structured Interviews Using Sensors and Algorithms

Draper Laboratory and MRAC have recently completed a comprehensive study to quantitatively evaluate deception detection performance under different interviewing styles. The interviews were performed while multiple physiological waveforms were collected from participants to determine how well automated algorithms can detect deception based upon changes in physiology. We report the results of a multi-factorial experiment with 77 human participants who were deceptive on specific topics during interviews conducted with one of two styles: a forcing style which relies on more coercive or confrontational techniques, or a fostering approach, which relies on open-ended interviewing and elements of a cognitive interview. The interviews were performed in a state-of-the-art facility where multiple sensors simultaneously collect synchronized physiological measurements, including electrodermal response, relative blood pressure, respiration, pupil diameter, and ECG. Features extracted from these waveforms during honest and deceptive intervals were then submitted to a hypothesis test to evaluate their statistical significance. A univariate statistical detection algorithm then assessed the ability to detect deception for different interview configurations. Our paper will explain the protocol and experimental design for this study. Our results will be in terms of statistical significances, effect sizes, and ROC curves and will identify how promising features performed in different interview scenarios.