Аннотация:Gaze-based interaction typically requires certain actions to confirm selections, which often makes interaction less convenient. Recently, effective identification of the user’s intention to make a gaze-based selection was demonstrated by Isomoto et al. (2022) using machine learning applied to gaze behavior features. However, a certain bias could appear in that study since the participants were requested to report their intentions during the interaction experiment. Here, we applied several classification algorithms (linear discriminant analysis, RBF and linear support vector machines, and random forest) to gaze features characterizing selections made in a freely played gaze-controlled game, in which moves were made by sequences of gaze-based selections and their gaze-based confirmations, without separate reporting the correctness of the selection. Intention to select was successfully predicted by each of the classifiers using features collected before the selection.
