EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

TL;DR

This paper introduces EllSeg, a convolutional neural network framework for directly segmenting elliptical eye structures, significantly improving robustness and accuracy in pupil and iris tracking under occlusions.

Contribution

The novel EllSeg framework enables direct ellipse segmentation, enhancing robustness and accuracy in gaze tracking compared to traditional eye part segmentation methods.

Findings

At least 10% increase in pupil center detection rate.

At least 24% increase in iris center detection rate.

Robust performance under occlusions and challenging conditions.

Abstract

Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10$\%$ and 24$\%$ increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.