Entropy-based correction of eye tracking data for static scenes

In a typical head-mounted eye tracking system, any small slippage of the eye tracker headband on the participant's head leads to a systematic error in the recorded gaze positions. While various approaches exist that reduce these errors at recording time, only few methods reduce the errors of a given tracking system after recording. In this paper we introduce a novel correction algorithm that can significantly reduce the drift in recorded gaze data for eye tracking experiments that use static stimuli. The algorithm is entropy-based and needs no prior knowledge about the stimuli shown or the tasks participants accomplish during the experiment.