EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

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.

arXiv commentCode available at https://bitbucket.org/RSKothari/ellseg/src/master/

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Cited by in corpus (4)

• EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking
• Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
• LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images
• Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality