The influence of pupil ellipse noise on the convergence time of a glint-free 3D eye tracking algorithm

Eye-tracking is a key sensing technology for upcoming retinal projection augmented reality (AR) glasses. State-of-the-art eye-tracking sensor technologies rely on video oculography (VOG) and 3D model based gaze estimation algorithms, which infer gaze from observations of the projected pupil over time. The convergence time of these algorithms relies heavily on the pupil ellipse fitting accuracy. In this work, we investigate the effects of pupil ellipse contour noise and pupil center noise on the convergence time of a state-of-the-art eye-tracking approach and show that the convergence time relies heavily on a sub-pixel accurate pupil ellipse fitting and can reach tens of seconds for inaccurately fitted ellipses.