Blink detection for real-time eye tracking

This work is motivated by our goal of providing non-contact head and eye based control of computer systems for people with motor difficulties. The system described here uses spatio-temporal filtering and variance maps to locate the head and find the eye-feature points respectively. These feature points are accurately tracked in the succeeding frames by using a modified version of the Lucas-Kanade tracking algorithm with pyramidal implementation. Accurate head and eye tracking results are obtained at a processing rate of more than 30 fps in more than 90% cases with a low false positive blink detection rate of 0.01%. This is achieved under varying lighting conditions for people of different ethnicity, with and without wearing glasses.