The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection

Abstract

We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.

arXiv comment6pages, ICDMW

Cited by in corpus (5)

• Introducing Representations of Facial Affect in Automated Multimodal Deception Detection
• Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection
• Deception Detection in Videos
• "Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
• Face-Focused Cross-Stream Network for Deception Detection in Videos