Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking

TL;DR

This paper presents a robust iris centre localization method using U-Net variants, significantly improving accuracy over previous Bayes' classifier approaches while maintaining real-time performance for eye-gaze tracking in unconstrained conditions.

Contribution

The paper introduces segmentation and regression-based U-Net approaches for iris localization, outperforming traditional Bayes' classifier methods in accuracy and robustness.

Findings

Comparable or better accuracy than state-of-the-art methods

Significant improvement over Bayes' classifier-based localization

Maintains real-time performance in unconstrained conditions

Abstract

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.