# Predicting Gender via Eye Movements

arXiv: https://arxiv.org/abs/2206.07442 · HTML: https://mlsanity.com/paper/2206.07442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [A Score-level Fusion Method for Eye Movement Biometrics](https://mlsanity.com/paper/1601.03333/) (2016)
- [An Extensive Study of User Identification via Eye Movements across Multiple Datasets](https://mlsanity.com/paper/2111.05901/) (2021)

## Abstract (arXiv)

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.