  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.
