Deception Detection using Real-life Trial Data

Abstract

Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stake nature of trial outcomes, implementing accurate and effective computational methods to evaluate the honesty of court testimonies can offer valuable support during the decision making process. In this paper, we address the identification of deception in real-life trial data. We introduce a novel dataset consisting of videos collected from public court trials. We explore the use of verbal and non-verbal modalities to build a multimodal deception detection system that aims to discriminate between truthful and deceptive statements provided by defendants and witnesses. We achieve classification accuracies in the range of 60-75% when using a model that extracts and fuses features from the linguistic and gesture modalities. In addition, we present a human deception detection study where we evaluate the human capability of detecting deception in trial hearings. The results show that our system outperforms the human capability of identifying deceit.