  Video-based eye tracking is a valuable technique in various research fields.
Numerous open-source eye tracking algorithms have been developed in recent
years, primarily designed for general application with many different camera
types. These algorithms do not, however, capitalize on the high frame rate of
eye tracking cameras often employed in psychophysical studies. We present a
pupil detection method that utilizes this high-speed property to obtain
reliable predictions through recursive estimation about certain pupil
characteristics in successive camera frames. These predictions are subsequently
used to carry out novel image segmentation and classification routines to
improve pupil detection performance. Based on results from hand-labelled eye
images, our approach was found to have a greater detection rate, accuracy and
speed compared to other recently published open-source pupil detection
algorithms. The program's source code, together with a graphical user
interface, can be downloaded at https://github.com/tbrouns/eyestalker
