Single Camera Remote Eye Gaze Tracking Under Natural Head Movements

Most available gaze tracking systems based on corneal reflection have low tolerance for head movements. In this paper, an eye-gaze tracking method, which can make this system be adaptive to external lighting changes and to the user’s head movements, is put forward to alleviate the restriction on the user’s head motion. We manage to improve the performance of the algorithm: The thresholds for extraction of the pupil and the corneal reflection are automated; the edge points of the pupil could be obtained by a 1D line search. To improve the resolution and accuracy of the system, the sub-pixel technique is used to locate the feature position accurately in the eye image. We apply a new randomized method of ellipse fitting to the edge points for good results. Our system can work under natural head movements by using only one camera to track eye-gaze. It can run in real time at video rate (25frames/second). A gaze accuracy of 1 degree is observed.