APPD: Adaptive and Precise Pupil Boundary Detection using Entropy of Contour Gradients
This work addresses the need for precise pupil detection in applications like ophthalmology, assistive technologies, and gaming, though it appears incremental as it builds on existing methods with adaptive heuristics.
The study tackled the problem of accurately detecting pupil boundaries and centers in eye images, especially under occlusions and oblique views, by proposing APPD, an adaptive method that achieves faster detection with an average execution time of ~5 ms for 720p images and outperforms three state-of-the-art algorithms.
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.