A New Robust Pupil Detection Algorithm for Eye Tracking Based Human-Computer Interface

In this paper a new pupil detection algorithm appropriate for development of head mounted eye tracking interfaces is presented. The robustness of the proposed algorithm is given by its low running time and the ability to accurately provide the coordinates of the pupil center even under real-world scenarios like variable illumination and high noise conditions. The proposed algorithm is based on an adaptive quantitative binarization technique with a feedback-loop controlled threshold. The pupil center is detected by applying the Least Square Fit to Ellipse procedure to the processed eye image. The performance of this algorithm is given by a 5 pixels detection rate of 93%, obtained for the eye images from two representative databases.