Motion tracking of iris features for eye tracking

Abstract

Current video-based eye trackers fail to acquire a high signal-to-noise (SNR) ratio which is crucial for specific applications like interactive systems, event detection, the study of various eye movements, and most importantly estimating the gaze position with high certainty. Specifically, current video-based eye trackers over-rely on precise localization of the pupil boundary and/or corneal reflection (CR) for gaze tracking, which often results in inaccuracies and large sample-to-sample root mean square (RMS-S2S). Therefore, it is crucial to address the shortcomings of these trackers, and we plan to study a new video-based eye tracking methodology focused on simultaneously tracking the motion of many iris features and investigate its implications for obtaining high accuracy and precision. In our preliminary work, the method has shown great potential for robust detection of microsaccades over 0.2 degrees with high confidence. Hence, we plan to explore and optimize this technique.