Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking
This work addresses a core problem in assistive eye-gaze tracking for users needing robust localization in unconstrained conditions, representing an incremental improvement.
The paper tackled robust iris centre localisation for eye-gaze tracking by applying U-Net variants, achieving results comparable to or better than state-of-the-art with a drastic improvement over a previous Bayes' classifier while maintaining real-time performance.
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.