Fast and robust pupil detection is an essential prerequisite for video-based eye-tracking in real-world settings. Several algorithms for image-based pupil detection have been proposed in the past, their applicability, however, is mostly limited to laboratory conditions. In real-world scenarios, automated pupil detection has to face various challenges, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. We propose  ElSe , a novel algorithm based on ellipse evaluation of a filtered edge image. We aim at a robust, inexpensive approach that can be integrated in embedded architectures, e.g., driving. The proposed algorithm was evaluated against four state-of-the-art methods on over 93,000 hand-labeled images from which 55,000 are new eye images contributed by this work. On average, the proposed method achieved a 14.53% improvement on the detection rate relative to the best state-of-the-art performer. Algorithm and data sets are available for download: ftp://emmapupildata@messor.informatik.uni-tuebingen.de (password: eyedata ).
['Wolfgang Fuhl', 'Thiago Santini', 'Thomas C. Kübler', 'Enkelejda Kasneci']
ElSe: ellipse selection for robust pupil detection in real-world environments