# EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

arXiv: https://arxiv.org/abs/2007.09600 · HTML: https://mlsanity.com/paper/2007.09600/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 24 of 24)

- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis](https://mlsanity.com/paper/2101.04318/) (2021)
- [1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning](https://mlsanity.com/paper/2102.01921/) (2021)
- [Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)](https://mlsanity.com/paper/2311.08077/) (2023)
- [EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking](https://mlsanity.com/paper/2205.01947/) (2022)
- [Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems](https://mlsanity.com/paper/2403.15947/) (2024)
- [Seeing the World through Your Eyes](https://mlsanity.com/paper/2306.09348/) (2023)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [Semi-Supervised Learning for Eye Image Segmentation](https://mlsanity.com/paper/2103.09369/) (2021)
- [CondSeg: Ellipse Estimation of Pupil and Iris via Conditioned Segmentation](https://mlsanity.com/paper/2408.17231/) (2024)
- [EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing](https://mlsanity.com/paper/2409.18813/) (2024)
- [Towards Unsupervised Eye-Region Segmentation for Eye Tracking](https://mlsanity.com/paper/2410.06131/) (2024)
- [Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality](https://mlsanity.com/paper/2403.19768/) (2024)
- [A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting](https://mlsanity.com/paper/2407.19269/) (2024)
- [FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Optimized Foveated Rendering System Performance in Virtual Reality](https://mlsanity.com/paper/2412.10456/) (2024)
- [An Embedded and Real-Time Pupil Detection Pipeline](https://mlsanity.com/paper/2302.14098/) (2023)
- [Robust Ellipsoid Fitting Using Axial Distance and Combination](https://mlsanity.com/paper/2304.00517/) (2023)
- [DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery](https://mlsanity.com/paper/2311.07235/) (2023)
- [Normalized Validity Scores for DNNs in Regression based Eye Feature Extraction](https://mlsanity.com/paper/2403.11665/) (2024)
- [EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR](https://mlsanity.com/paper/2507.09649/) (2025)
- [Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling](https://mlsanity.com/paper/2603.14077/) (2026)
- [Eye image segmentation using visual and concept prompts with Segment Anything Model 3 (SAM3)](https://mlsanity.com/paper/2603.17715/) (2026)
- [AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination](https://mlsanity.com/paper/2606.03774/) (2026)
- [Event-Based Motion Estimation via Oriented Distance Fields](https://mlsanity.com/paper/2608.24223/) (2026)

## Abstract (arXiv)

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.