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

TL;DR

This paper introduces a new annotated facial dataset from a card game to evaluate machine learning lie detectors, showing deep learning models achieve up to 63% accuracy but face cross-domain limitations.

Contribution

The work provides a novel dataset with 2D and 3D facial data during lying tasks and evaluates machine learning models, highlighting their strengths and limitations.

Findings

Deep learning models reach 57% accuracy in generalization tasks.

Models achieve up to 63% accuracy for single participants.

Cross-domain detection remains a significant challenge.

Abstract

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.