Gender Classification of Prepubescent Children via Eye Movements with Reading Stimuli
Sahar Mahdie Klim Al Zaidawi, Martin H.U. Prinzler, Christoph Schröder, Gabriel Zachmann, Sebastian Maneth · Companion Publication of the 2020 International Conference on Multimodal Interaction · 2020
We present a new study of gender prediction using eye movements of prepubescent children aged 9--10. Despite previous research indicating that gender differences in eye movements are observed only in adults, we are able to predict gender with accuracies of up to 64%. Our method segments gaze point trajectories into saccades and fixations. It then computes a small number of features and classifies saccades and fixations separately using statistical methods. The used dataset contains non-dyslexic and dyslexic children. In mixed groups, the accuracy of our classifiers drops dramatically. To address this challenge, we construct a hierarchical classifier that makes use of dyslexia prediction to improve significantly the accuracy of gender prediction in mixed groups.