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In this paper we present a prototype for an automated deception detection system. Similar to polygraph examinations, we
attempt to take advantage of the theory that false answers will produce distinctive measurements in certain physiological
manifestations. We investigate the role of dynamic eye-based features such as eye closure/blinking and lateral movements
of the iris in detecting deceit. The features are recorded both when the test subjects are having non-threatening conversations
as well as when they are being interrogated about a crime they might have committed. The rates of the behavioral
changes are blindly clustered into two groups. Examining the clusters and their characteristics, we observe that the dynamic
features selected for deception detection show promising results with an overall deceptive/non-deceptive prediction
rate of 71.43% from a study consisting of 28 subjects.
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Ifeoma Nwogu, Mark Frank, Venu Govindaraju, "An automated process for deceit detection," Proc. SPIE 7667, Biometric Technology for Human Identification VII, 76670R (14 April 2010); https://doi.org/10.1117/12.851407