Enhancing the precision of remote eye-tracking using iris velocity estimation

ABSTRACT Most of the previous work on eye-tracking has focused on positional information of the eye features. Recent advances in camera technology such as high-resolution and event cameras allow consideration of the velocity estimate for eye tracking. Some previous work on velocity-based estimates has demonstrated high-precision gaze estimation by tracking the motion of iris features on high-resolution images rather than by exploiting pupil edges. While these methods provide high precision, the bottleneck for velocity-based methods are temporal drift and the inability to track across blinks. In this work, we present a new theoretical methodology (πt) to address these issues by optimally combining low-temporal frequency components of the pupil edges with the high-temporal frequency components from the iris textures. We show improved precision with this method while fixating a series of small targets and following a smoothly moving target. Further, we demonstrate the capability to reliably identify microsaccades between targets separated by 0.2°.