ESCaF: Pupil Centre Localization Algorithm with Candidate Filtering
This addresses pupil localization challenges for gaze tracking applications in uncontrolled environments like with glasses or motion blur.
The researchers tackled the problem of pupil center localization for gaze tracking in real-world conditions, proposing ESCaF which uses edge and intensity information with candidate filtering to improve detection rates. Their algorithm outperformed state-of-the-art methods on the LPW dataset while achieving real-time performance.
Algorithms for accurate localization of pupil centre is essential for gaze tracking in real world conditions. Most of the algorithms fail in real world conditions like illumination variations, contact lenses, glasses, eye makeup, motion blur, noise, etc. We propose a new algorithm which improves the detection rate in real world conditions. The proposed algorithm uses both edges as well as intensity information along with a candidate filtering approach to identify the best pupil candidate. A simple tracking scheme has also been added which improves the processing speed. The algorithm has been evaluated in Labelled Pupil in the Wild (LPW) dataset, largest in its class which contains real world conditions. The proposed algorithm outperformed the state of the art algorithms while achieving real-time performance.