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

TL;DR

This paper introduces a multimodal, data-driven approach for deception detection in real-life trial data by analyzing visual facial cues, acoustic patterns, and lexical features, and combining them through fusion techniques.

Contribution

It presents an integrated method combining facial action units, acoustic analysis, and lexical cues with fusion strategies for improved deception detection accuracy.

Findings

Facial action units reveal subtle facial movements associated with deception.

Lexical cues like pauses significantly contribute to deception prediction.

Fusion of visual and lexical features enhances detection performance.

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.