ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments

TL;DR

ElSe is a new ellipse-based pupil detection algorithm designed for robustness in real-world environments, outperforming existing methods by over 14% in detection rate on a large, diverse dataset.

Contribution

The paper introduces ElSe, a novel, resource-efficient pupil detection algorithm based on ellipse evaluation, suitable for embedded systems in challenging real-world conditions.

Findings

Achieved 14.53% higher detection rate than state-of-the-art methods.

Evaluated on over 93,000 images, including 55,000 new images.

Demonstrated robustness against illumination changes, reflections, make-up, and physiological variations.

Abstract

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, their applicability is mostly limited to laboratory conditions. In realworld 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, resource-saving 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 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. download:ftp://emmapupildata@messor.informatik.unituebingen. de (password:eyedata).