  Lies and deception are common phenomena in society, both in our private and
professional lives. However, humans are notoriously bad at accurate deception
detection. Based on the literature, human accuracy of distinguishing between
lies and truthful statements is 54% on average, in other words it is slightly
better than a random guess. While people do not much care about this issue, in
high-stakes situations such as interrogations for series crimes and for
evaluating the testimonies in court cases, accurate deception detection methods
are highly desirable. To achieve a reliable, covert, and non-invasive deception
detection, we propose a novel method that jointly extracts reliable low- and
high-level facial features namely, 3D facial geometry, skin reflectance,
expression, head pose, and scene illumination in a video sequence. Then these
features are modeled using a Recurrent Neural Network to learn temporal
characteristics of deceptive and honest behavior. We evaluate the proposed
method on the Real-Life Trial (RLT) dataset that contains high-stake deceptive
and honest videos recorded in courtrooms. Our results show that the proposed
method (with an accuracy of 72.8%) improves the state of the art as well as
outperforming the use of manually coded facial attributes 67.6%) in deception
detection.
