In this research work, we address the problem of robust iris centre localisation in unconstrained conditions as a core component of our eye-gaze tracking platform. We investigate the application of U-Net variants for segmentation-based and regression-based approaches to improve our iris centre localisation, which was previously based on Bayes' classification. The achieved results are comparable to or better than the state-of-the-art, offering a drastic improvement over those achieved by the Bayes' classifier, and without sacrificing the real-time performance of our eye-gaze tracking platform.
@article{arxiv.2411.04912,
title = {Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking},
author = {Nipun Sandamal Ranasekara Pathiranage and Stefania Cristina and Kenneth P. Camilleri},
journal= {arXiv preprint arXiv:2411.04912},
year = {2024}
}