The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection
This work addresses the problem of identifying deception in legal settings, but it is incremental as it combines existing methods like OpenFace and OpenSmile with a standard classifier.
The authors tackled automatic deception detection in real-life trial data by analyzing visual, acoustic, and lexical cues, achieving a prediction accuracy of 85% for deceit or truth using a Support Vector Machine.
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.