PuReST

Abstract

Pervasive eye-tracking applications such as gaze-based human computer interaction and advanced driver assistance require real-time, accurate, and robust pupil detection. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges - for instance, quickly changing illumination and occlusions. In this work, we introduce the Pupil Reconstructor with Subsequent Tracking (PuReST), a novel method for fast and robust pupil tracking. The proposed method was evaluated on over 266,000 realistic and challenging images acquired with three distinct head-mounted eye tracking devices, increasing pupil detection rate by 5.44 and 29.92 percentage points while reducing average run time by a factor of 2.74 and 1.1. w.r.t. state-of-the-art 1) pupil detectors and 2) vendor provided pupil trackers, respectively. Overall, PuReST outperformed other methods in 81.82% of use cases.