An Adaptive Algorithm for Precise Pupil Boundary Detection using Entropy of Contour Gradients

Eye tracking spreads through a vast area of applications from ophthalmology, assistive technologies to gaming and virtual reality. Detection of pupil is the most critical step in many of these tasks hence needs to be performed accurately. Although detection of pupil is a smooth task in clear sight, possible occlusions and odd viewpoints complicate the problem. We present an adaptive pupil boundary detection method that is able to infer whether entire pupil is in clearly visible by a modal heuristic. Thus, a faster detection is performed with the assumption of no occlusions. If the heuristic fails, a deeper search among extracted image features is executed to maintain accuracy. Furthermore, the algorithm can find out if there is no pupil as an aidful information for many applications. We prepare a dataset containing 1509 high resolution eye images collected from five 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 can run up to 140 Hz in single-thread on a standard laptop computer.