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Machine learning-based lie detector applied to a novel annotated game dataset

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dc.contributor.author Rodriguez-Diaz, Nuria
dc.contributor.author Aspandi, Decky
dc.contributor.author Sukno, Federico Mateo
dc.contributor.author Binefa, Xavier
dc.date.accessioned 2023-02-23T07:08:48Z
dc.date.available 2023-02-23T07:08:48Z
dc.date.issued 2022
dc.identifier.citation Rodriguez-Diaz N, Aspandi D, Sukno FM, Binefa X. Machine learning-based lie detector applied to a novel annotated game dataset. Future Internet. 2022;14(1):2. DOI: 10.3390/fi14010002
dc.identifier.issn 1999-5903
dc.identifier.uri http://hdl.handle.net/10230/55877
dc.description.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 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 actual intentions of individuals can be very important.
dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under project grant TIN2017-90124-P, the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), the donation bahi2018-19 to the CMTech at UPF, and UDeco project by Germany BMBF-KMU Innovativ.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartof Future Internet. 2022;14(1):2.
dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Machine learning-based lie detector applied to a novel annotated game dataset
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/fi14010002
dc.subject.keyword lie detection
dc.subject.keyword machine learning
dc.subject.keyword affective computing
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90124-P
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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