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

arXiv: https://arxiv.org/abs/1903.04484 · HTML: https://mlsanity.com/paper/1903.04484/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection](https://mlsanity.com/paper/2102.03673/) (2021)
- [Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective](https://mlsanity.com/paper/2211.13035/) (2022)
- [Constructing Robust Emotional State-based Feature with a Novel Voting Scheme for Multi-modal Deception Detection in Videos](https://mlsanity.com/paper/2104.08373/) (2021)
- [Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset](https://mlsanity.com/paper/2104.12345/) (2021)
- [Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection](https://mlsanity.com/paper/2108.07897/) (2021)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [Voting-based Multimodal Automatic Deception Detection](https://mlsanity.com/paper/2307.07516/) (2023)

## Abstract (arXiv)

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.