Machine learning classification of user attributes via eye movements

The advent of modern eye tracking devices has spawned a plethora of new research
on eye movements. Applications of these research results include the prediction of
diseases, of biometrics, of gender, or of cognitive developments in children. One par-
ticularly well studied topic is user identification. Another, less well studied one is
gender prediction. In this thesis, a common framework to predict users and gen-
der is proposed. Using this framework, we were able to improve the state-of-the-art
accuracies for both user identification and gender prediction. Further, unlike previ-
ous studies, the proposed approach was tested with different datasets consisting of
varying stimuli. We identify several factors that affect the identification accuracy.
Our main improvements in identification accuracy are due to three factors, select-
ing optimal hyper-parameters of the segmentation algorithm, adding higher-order
derivatives, and including blink information. For gender prediction, the thesis es-
tablishes several new insights. For instance, that gender prediction is possible for
prepubescent children aged 9–10. Previous research had suggested that significant
gender differences in eye movements can only be observed in adults. Various factors
are identified which affect the accuracy of gender prediction; for example, the length
of the gaze trajectory, possible fatigue of the participant (gender prediction works
better in the presence of fatigue), and the choice of feature ranking algorithms.