High-Stakes Deception Detection Based on Facial Expressions

During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that might result. This paper proposes an automated computer vision solution for detecting high-stakes deception based on facial clues. Four deceptive cues (eye-blink, eyebrow motion, wrinkle occurrence and mouth motion) were identified and integrated into a single facial behavior pattern vector for discerning deception and honesty. A Random Forest classifier was trained using an unconstrained video database and applied to classify facial patterns into either deceptive or truthful categories. The labeled database we created was based on open sources such as YouTube. The interview videos used for training and testing the classifier were selected on the basis of high-stakes criminal situations, such as murder or kidnapping, which were later verified by criminal trials. Despite the many uncontrolled factors (illumination, head pose and facial occlusion) in the videos, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers. This compares well with 80.9% [1], the best extant accurary obtained by experienced interrogators.