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Predicting Gender via Eye Movements

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HCI International 2022 – Late Breaking Posters (HCII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1654))

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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.

R. Haria and S. Al Zaidawi contributed equally to the work.

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Notes

  1. 1.

    The GOF dataset can be found here [1].

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Acknowledgement

We would like to thank Shubham Ajay Soukhiya for his contribution towards this work.

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Authors and Affiliations

  1. Department of Informatics in University of Bremen, Bremen, Germany

    Rishabh Vallabh Varsha Haria, Sahar Mahdie Klim Al Zaidawi & Sebastian Maneth

Authors
  1. Rishabh Vallabh Varsha Haria
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  2. Sahar Mahdie Klim Al Zaidawi
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  3. Sebastian Maneth
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Corresponding author

Correspondence to Rishabh Vallabh Varsha Haria .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  4. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

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Vallabh Varsha Haria, R., Mahdie Klim Al Zaidawi, S., Maneth, S. (2022). Predicting Gender via Eye Movements. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1654. Springer, Cham. https://doi.org/10.1007/978-3-031-19679-9_13

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  • DOI: https://doi.org/10.1007/978-3-031-19679-9_13

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