We revisit the problem of iris tracking with RGB cameras, aiming to obtain iris contours from captured images of eyes. We find the reason that limits the performance of the state-of-the-art method in more general non-cooperative environments, which prohibits a wider adoption of this useful technique in practice. We believe that because the iris boundary could be inherently unclear and blocked, as its pixels occupy only an extremely limited percentage of those on the entire image of the eye, similar to the stars hidden in fireworks, we should not treat the boundary pixels as one class to conduct end-to-end recognition directly. Thus, we propose to learn features from iris and sclera regions first, and then leverage entropy to sketch the thin and sharp iris boundary pixels, where we can trace more precise parameterized iris contours. In this work, we also collect a new dataset by smartphone with 22 K images of eyes from video clips. We annotate a subset of 2 K images, so that label propagation can be applied to further enhance the system performance. Extensive experiments over both public and our own datasets show that our method outperforms the state-of-the-art method. The results also indicate that our method can improve the coarsely labeled data to enhance the iris contour’s accuracy and support the downstream application better than the prior method.

中文翻译：

---

从烟花中寻找星星：改进非合作虹膜跟踪


我们重新审视了 RGB 相机的虹膜跟踪问题，旨在从捕获的眼睛图像中获取虹膜轮廓。我们找到了限制最先进方法在更一般的非合作环境中性能的原因，这阻碍了在实践中更广泛地采用这种有用的技术。我们认为，由于虹膜边界本质上是不清晰和被遮挡的，因为它的像素只占整个眼睛图像中极其有限的百分比，类似于烟花中隐藏的星星，所以我们不应该将边界像素视为一个类直接进行端到端识别。因此，我们建议首先从虹膜和巩膜区域学习特征，然后利用熵来绘制薄而清晰的虹膜边界像素，在这里我们可以跟踪更精确的参数化虹膜轮廓。在这项工作中，我们还通过智能手机收集了一个新的数据集，其中包含来自视频剪辑的 22 K 眼睛图像。我们对 2K 图像的子集进行注释，以便可以应用标签传播来进一步增强系统性能。对公共数据集和我们自己的数据集进行的广泛实验表明，我们的方法优于最先进的方法。结果还表明，与现有方法相比，我们的方法可以改进粗略标记的数据，以提高虹膜轮廓的准确性并更好地支持下游应用。