Real time eye tracking for human computer interfaces

In recent years considerable interest has developed in real time eye tracking for various applications. An approach that has received a lot of attention is the use of infrared technology for purposes of eye tracking. In this paper we propose a technique that does not rely on the use of infrared devices for eye tracking. Instead, our eye tracker makes use of a binary classifier with a dynamic training strategy and an unsupervised clustering stage in order to efficiently track the pupil (eyeball) in real time. The dynamic training strategy makes the algorithm subject (speaker) and lighting condition invariant. Our algorithm does not make any assumption regarding the position of the speaker's face in the field of view of the camera, nor does it restrict the 'natural' motion of the speaker in the field of view of the camera. Experimental results from a real time implementation show that this algorithm is robust and able to detect the pupils under various illumination conditions.