Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset
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 to their visual appearance, including the face, to find any signs that indicate whether or not the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/1/2 |
| _version_ | 1797493949271638016 |
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| author | Nuria Rodriguez-Diaz Decky Aspandi Federico M. Sukno Xavier Binefa |
| author_facet | Nuria Rodriguez-Diaz Decky Aspandi Federico M. Sukno Xavier Binefa |
| author_sort | Nuria Rodriguez-Diaz |
| collection | DOAJ |
| description | 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 to their visual appearance, including the face, to find any signs that indicate whether or not the person is telling the truth. While automatic lie detection may help us to understand these 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 collect 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 evaluate several types of machine learning-based lie detectors in terms of their generalization, in person-specific and cross-application experiments. We first extract both handcrafted and deep learning-based features as relevant visual inputs, then pass them into multiple types of classifier to predict respective lie/non-lie labels. Subsequently, we use several metrics to judge the models’ accuracy based on the models predictions and ground truth. In our experiment, we show that models based on deep learning achieve the highest accuracy, reaching up to 57% for the generalization task and 63% when applied to detect the lie to a single participant. We further highlight the limitation of the deep learning-based lie detector when dealing with cross-application lie detection tasks. Finally, this analysis along the proposed datasets would potentially be useful not only from the perspective of computational systems perspective (e.g., improving current automatic lie prediction accuracy), but also for other relevant application fields, such as health practitioners in general medical counselings, education in academic settings or finance in the banking sector, where close inspections and understandings of the <i>actual</i> intentions of individuals can be very important. |
| first_indexed | 2024-03-10T01:27:19Z |
| format | Article |
| id | doaj.art-f53d57e4a7e64d5291ea15e40172cb06 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T01:27:19Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-f53d57e4a7e64d5291ea15e40172cb062023-11-23T13:49:13ZengMDPI AGFuture Internet1999-59032021-12-01141210.3390/fi14010002Machine Learning-Based Lie Detector Applied to a Novel Annotated Game DatasetNuria Rodriguez-Diaz0Decky Aspandi1Federico M. Sukno2Xavier Binefa3Department of Information and Communication Technology, Universitat Pompeu Fabra, 08026 Barcelona, SpainDepartment of Information and Communication Technology, Universitat Pompeu Fabra, 08026 Barcelona, SpainDepartment of Information and Communication Technology, Universitat Pompeu Fabra, 08026 Barcelona, SpainDepartment of Information and Communication Technology, Universitat Pompeu Fabra, 08026 Barcelona, SpainLie 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 to their visual appearance, including the face, to find any signs that indicate whether or not the person is telling the truth. While automatic lie detection may help us to understand these 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 collect 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 evaluate several types of machine learning-based lie detectors in terms of their generalization, in person-specific and cross-application experiments. We first extract both handcrafted and deep learning-based features as relevant visual inputs, then pass them into multiple types of classifier to predict respective lie/non-lie labels. Subsequently, we use several metrics to judge the models’ accuracy based on the models predictions and ground truth. In our experiment, we show that models based on deep learning achieve the highest accuracy, reaching up to 57% for the generalization task and 63% when applied to detect the lie to a single participant. We further highlight the limitation of the deep learning-based lie detector when dealing with cross-application lie detection tasks. Finally, this analysis along the proposed datasets would potentially be useful not only from the perspective of computational systems perspective (e.g., improving current automatic lie prediction accuracy), but also for other relevant application fields, such as health practitioners in general medical counselings, education in academic settings or finance in the banking sector, where close inspections and understandings of the <i>actual</i> intentions of individuals can be very important.https://www.mdpi.com/1999-5903/14/1/2lie detectionmachine learningaffective computing |
| spellingShingle | Nuria Rodriguez-Diaz Decky Aspandi Federico M. Sukno Xavier Binefa Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset Future Internet lie detection machine learning affective computing |
| title | Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset |
| title_full | Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset |
| title_fullStr | Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset |
| title_full_unstemmed | Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset |
| title_short | Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset |
| title_sort | machine learning based lie detector applied to a novel annotated game dataset |
| topic | lie detection machine learning affective computing |
| url | https://www.mdpi.com/1999-5903/14/1/2 |
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