Eye Movements as Indicators of Deception: A Machine Learning Approach

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dc.contributor.authorFoucher, Valentincs
dc.contributor.authorde Leon Martinez, Santiago Josecs
dc.contributor.authorMoro, Robertcs
dc.date.accessioned2025-07-17T08:59:26Z
dc.date.available2025-07-17T08:59:26Z
dc.date.issued2025-05-25cs
dc.description.abstractGaze may enhance the robustness of lie detectors, but remains under-studied. This study evaluated the efficacy of AI models (using fixations, saccades, blinks, and pupil size) for detecting deception in Concealed Information Tests across two datasets. The first, collected with Eyelink 1000, contains gaze data from a computerized experiment in which 87 participants revealed, concealed, or faked the value of a previously selected card. The second, collected with Pupil Neon, involved 37 participants performing a similar task but facing an experimenter. AI models (XGBoost) achieved accuracies of up to 74\% in a binary classification task (Revealing vs. Concealing) and 49\% in a more challenging three-classification task (Revealing vs. Concealing vs. Faking). Feature analysis identified saccade number, duration and amplitude along with maximum pupil size as the most important for deception prediction. These results demonstrate the feasibility of using gaze and AI to enhance lie detectors and encourage future research that may improve on this.en
dc.description.abstractGaze can enhance the robustness of lie detectors, but remains understudied. This study evaluated the effectiveness of AI models (using fixations, saccades, blinks, and pupil size) for detecting deception in hidden information tests in two datasets. The first, collected with the Eyelink 1000, contains gaze data from a computer experiment in which 87 participants revealed, concealed, or faked the value of a previously selected card. The second, collected with the Pupil Neon, included 37 participants performing a similar task but facing an experimenter. The AI models (XGBoost) achieved up to 74% accuracy in a binary classification task (Reveal vs. Hide) and 49% in a more challenging three-classification task (Reveal vs. Hide vs. Fake). Feature analysis identified saccade count, duration, and amplitude, along with maximum pupil size, as the most important for predicting deception. These results demonstrate the feasibility of using vision and artificial intelligence to improve lie detectors and will encourage future research that could improve this.cs
dc.formattextcs
dc.format.extent1-7cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationETRA '25: Proceedings of the 2025 Symposium on Eye Tracking Research and Applications. 2025, p. 1-7.en
dc.identifier.doi10.1145/3715669.3723129cs
dc.identifier.other194215cs
dc.identifier.urihttps://hdl.handle.net/11012/255187
dc.language.isoencs
dc.publisherACMcs
dc.relation.ispartofETRA '25: Proceedings of the 2025 Symposium on Eye Tracking Research and Applicationscs
dc.relation.urihttps://dl.acm.org/doi/full/10.1145/3715669.3723129cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectEye Movementsen
dc.subjectGazeen
dc.subjectPupilen
dc.subjectDeception Detectionen
dc.subjectConcealed Information Testen
dc.subjectMachine Learningen
dc.subject<br>Feature Importanceen
dc.subjectEye Movements
dc.subjectGaze
dc.subjectPupil
dc.subjectDeception Detection
dc.subjectConcealed Information Test
dc.subjectMachine Learning
dc.subject<br>Feature Importance
dc.titleEye Movements as Indicators of Deception: A Machine Learning Approachen
dc.title.alternativePohyby očí jako indikátory podvodu: Přístup strojového učenícs
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-194215en
sync.item.dbtypeVAVen
sync.item.insts2025.07.17 10:59:26en
sync.item.modts2025.07.17 10:33:55en
thesis.grantorVysoké učení technické v Brně. Fakulta informačních technologií. Ústav počítačové grafiky a multimédiícs
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