用U-Net提升眼球虹膜中心定位，更准更快。
Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking
- 采用U-Net变体做分割与回归，替代原有贝叶斯分类器。
- 定位精度达业界领先水平，且保持实时性能。
- 适合无障碍眼动追踪系统开发人员参考。
本研究针对非受限条件下虹膜中心定位的鲁棒性问题，将其作为眼动追踪平台的核心组件进行优化。我们探索了U-Net变体在基于分割与基于回归方法中的应用，以改进此前依赖贝叶斯分类器的虹膜中心定位方法。实验结果表明，该方法在精度上达到或超越当前最优水平，显著优于原贝叶斯分类器，同时未牺牲眼动追踪系统的实时性能。
原文摘要 · Abstract (English)
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.
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