  Real-time, accurate, and robust pupil detection is an essential prerequisite
for pervasive video-based eye-tracking. However, automated pupil detection in
real-world scenarios has proven to be an intricate challenge due to fast
illumination changes, pupil occlusion, non centered and off-axis eye recording,
and physiological eye characteristics. In this paper, we propose and evaluate a
method based on a novel dual convolutional neural network pipeline. In its
first stage the pipeline performs coarse pupil position identification using a
convolutional neural network and subregions from a downscaled input image to
decrease computational costs. Using subregions derived from a small window
around the initial pupil position estimate, the second pipeline stage employs
another convolutional neural network to refine this position, resulting in an
increased pupil detection rate up to 25% in comparison with the best performing
state-of-the-art algorithm. Annotated data sets can be made available upon
request.
