  We built a custom video-based eye-tracker that saves every video frame as a
full resolution image (MJPEG). Images can be processed offline for the
detection of ocular features, including the pupil and corneal reflection (First
Purkinje Image, P1) position. A comparison of multiple algorithms for detection
of pupil and corneal reflection can be performed. The system provides for
highly flexible stimulus creation, with mixing of graphic, image, and video
stimuli. We can change cameras and infrared illuminators depending on the image
qualities and frame rate desired. Using this system, we have detected the
position of the Fourth Purkinje image (P4) in the frames. We show that when we
estimate gaze by calculating P1-P4, signal compares well with gaze estimated
with a DPI eye-tracker, which natively detects and tracks the P1 and P4.
