# Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset

arXiv: https://arxiv.org/abs/2104.12345 · HTML: https://mlsanity.com/paper/2104.12345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [A Deep Learning Approach for Multimodal Deception Detection](https://mlsanity.com/paper/1803.00344/) (2018)
- [Face-Focused Cross-Stream Network for Deception Detection in Videos](https://mlsanity.com/paper/1812.04429/) (2018)
- [Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection](https://mlsanity.com/paper/1611.10152/) (2016)
- [The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection](https://mlsanity.com/paper/1903.04484/) (2019)
- [End-to-end facial and physiological model for Affective Computing and applications](https://mlsanity.com/paper/1912.04711/) (2019)
- [An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild](https://mlsanity.com/paper/2102.09150/) (2021)

## Built on by (most cited first, 1 of 1)

- [Predicting Eye Gaze Location on Websites](https://mlsanity.com/paper/2211.08074/) (2022)

## Abstract (arXiv)

Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and also to their visual appearances, including faces, to try to find any signs that indicate whether the person is telling the truth or not. While automatic lie detection may help us to understand this lying characteristics, current systems are still fairly limited, partly due to lack of adequate datasets to evaluate their performance in realistic scenarios. In this work, we have collected an annotated dataset of facial images, comprising both 2D and 3D information of several participants during a card game that encourages players to lie. Using our collected dataset, We evaluated several types of machine learning-based lie detectors in terms of their generalization, person-specific and cross-domain experiments. Our results show that models based on deep learning achieve the best accuracy, reaching up to 57% for the generalization task and 63% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with cross-domain lie detection tasks.