Eye tracking using Markov models

We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method, a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subject's gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.