Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset
This work addresses lie detection for general human interaction applications, but it is incremental as it primarily introduces a new dataset and applies existing methods.
The authors tackled the problem of automatic lie detection by collecting a novel annotated dataset of facial images from a card game that encourages lying, and evaluated machine learning models on it. They found that deep learning models achieved up to 57% accuracy for generalization and 63% for person-specific tasks, but noted limitations in cross-domain detection.
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.