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.

翻译：在本研究工作中，我们解决了非约束条件下鲁棒虹膜中心定位的问题，该问题是我们眼动追踪平台的核心组件。我们研究了U-Net变体在基于分割和基于回归方法中的应用，以改进先前基于贝叶斯分类的虹膜中心定位技术。所取得的成果与当前最优水平相当或更优，相较于贝叶斯分类器实现了显著提升，且未牺牲我们眼动追踪平台的实时性能。