Abstract
Eye tracking spreads through a vast area of applications from ophthalmology, assistive technologies to gaming and virtual reality. Precisely detecting the pupil's contour and center is the very first step in many of these tasks, hence needs to be performed accurately. Although detection of pupil is a simple problem when it is entirely visible; occlusions and oblique view angles complicate the solution. In this study, we propose APPD, an adaptive and precise pupil boundary detection method that is able to infer whether entire pupil is in clearly visible by a heuristic that estimates the shape of a contour in a computationally efficient way. Thus, a faster detection is performed with the assumption of no occlusions. If the heuristic fails, a more comprehensive search among extracted image features is executed to maintain accuracy. Furthermore, the algorithm can find out if there is no pupil as an helpful information for many applications. We provide a dataset containing 3904 high resolution eye images collected from 12 subjects and perform an extensive set of experiments to obtain quantitative results in terms of accuracy, localization and timing. The proposed method outperforms three other state of the art algorithms and has an average execution time $\sim$5 ms in single-thread on a standard laptop computer for 720p images.
Abstract (translated)
眼动追踪通过眼科，辅助技术，游戏和虚拟现实等广泛应用。精确检测瞳孔的轮廓和中心是许多这些任务的第一步，因此需要准确地执行。虽然在完全可见的情况下检测瞳孔是一个简单的问题;闭塞和倾斜视角使解决方案复杂化。在这项研究中，我们提出APPD，一种自适应和精确的瞳孔边界检测方法，能够通过以计算有效的方式估计轮廓形状的启发式来推断整个瞳孔是否清晰可见。因此，在没有遮挡的情况下执行更快的检测。如果启发式失败，则执行提取的图像特征中的更全面的搜索以保持准确性。此外，该算法可以找出是否没有瞳孔作为许多应用的有用信息。我们提供了一个数据集，其中包含从12个受试者收集的3904个高分辨率眼睛图像，并执行一系列广泛的实验，以获得准确性，定位和时间方面的定量结果。所提出的方法优于其他三种最先进的算法，并且在用于720p图像的标准膝上型计算机上的单线程中具有平均执行时间$ \ sim $ 5ms。
URL
https://arxiv.org/abs/1709.06366
