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Computer Science > Machine Learning

arXiv:2206.07442 (cs)
[Submitted on 15 Jun 2022]

Title:Predicting Gender via Eye Movements

Authors:Rishabh Vallabh Varsha Haria, Sahar Mahdie Klim Al Zaidawi, Sebastian Maneth
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Abstract:In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2206.07442 [cs.LG]
  (or arXiv:2206.07442v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2206.07442

Submission history

From: Rishabh Haria [view email]
[v1] Wed, 15 Jun 2022 10:36:13 UTC (2,168 KB)
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