Predicting Gender via Eye Movements

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.

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