Identity Unbiased Deception Detection by 2D-to-3D Face Reconstruction

Wei Wang

Abstract:

Deception is a common phenomenon 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 disentangles facial expression
and head pose related features using 2D-to-3D face reconstruction technique from a video sequence and uses them to
learn characteristics of deceptive behavior. We evaluate the
proposed method on the Real-Life Trial (RLT) dataset that
contains high-stakes deceits recorded in courtrooms. Our
results show that the proposed method (with an accuracy of
68%) improves the state of the art. Besides, a new dataset
has been collected, for the first time, for low-stake deceit detection. In addition, we compare high-stake deceit detection
methods on the newly collected low-stake deceits