Eye Movements as Indicators of Deception: A Machine Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0194215" target="_blank" >RIV/00216305:26230/26:0194215 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/full/10.1145/3715669.3723129" target="_blank" >https://dl.acm.org/doi/full/10.1145/3715669.3723129</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3715669.3723129" target="_blank" >10.1145/3715669.3723129</a>
Alternative languages
Result language
angličtina
Original language name
Eye Movements as Indicators of Deception: A Machine Learning Approach
Original language description
Gaze 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 where 87 participants revealed, concealed, or faked the value of a previously selected card. The second, collected with Pupil Neon, involved 36 participants performing a similar task but facing an experimenter. XGBoost achieved accuracies 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, amplitude, and 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ETRA '25: Proceedings of the 2025 Symposium on Eye Tracking Research and Applications
ISBN
979-8-4007-1487-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1-7
Publisher name
ACM
Place of publication
New York
Event location
Tokyo
Event date
May 26, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001528457500014