  Algorithms for accurate localization of pupil centre is essential for gaze
tracking in real world conditions. Most of the algorithms fail in real world
conditions like illumination variations, contact lenses, glasses, eye makeup,
motion blur, noise, etc. We propose a new algorithm which improves the
detection rate in real world conditions. The proposed algorithm uses both edges
as well as intensity information along with a candidate filtering approach to
identify the best pupil candidate. A simple tracking scheme has also been added
which improves the processing speed. The algorithm has been evaluated in
Labelled Pupil in the Wild (LPW) dataset, largest in its class which contains
real world conditions. The proposed algorithm outperformed the state of the art
algorithms while achieving real-time performance.
