Eye Detection and Tracking Using Rectangle Features and Integrated Eye Tracker by Web Camera

Eye detection and tracking can be used in intelligent human-computer interfaces, driver drowsiness detection, security, and biology systems. In this paper a new method for eye detection based on some new rectangle features is proposed, with these features the Adaboost cascade classifiers are trained for eye detection. Then with the characteristics of symmetry of the eyes some of the geometric characteristics are adopted for correction. The geometric characteristics improve the accuracy of the eye detection, and make the rough cascade classifier trained by few samples become a reality in application. In this paper, we present an integrated eye tracker to overcome the effect of eye closure and external illumination by combining Kalman filter with Mean Shift algorithm. Results from an extensive experiment show a significant improvement of our technique over existing eye tracking techniques.