  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.

Machine Learning