# CondSeg: Ellipse Estimation of Pupil and Iris via Conditioned Segmentation

arXiv: https://arxiv.org/abs/2408.17231 · HTML: https://mlsanity.com/paper/2408.17231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings](https://mlsanity.com/paper/1805.04771/) (2018)
- [RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking](https://mlsanity.com/paper/1910.00694/) (2019)
- [TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types](https://mlsanity.com/paper/2102.02115/) (2021)
- [OpenEDS: Open Eye Dataset](https://mlsanity.com/paper/1905.03702/) (2019)
- [EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking](https://mlsanity.com/paper/2007.09600/) (2020)
- [OpenEDS2020: Open Eyes Dataset](https://mlsanity.com/paper/2005.03876/) (2020)
- [PuRe: Robust pupil detection for real-time pervasive eye tracking](https://mlsanity.com/paper/1712.08900/) (2017)
- [RIT-Eyes: Rendering of near-eye images for eye-tracking applications](https://mlsanity.com/paper/2006.03642/) (2020)
- [Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks](https://mlsanity.com/paper/1901.01028/) (2019)
- [Semi-Supervised Learning for Eye Image Segmentation](https://mlsanity.com/paper/2103.09369/) (2021)
- [Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework](https://mlsanity.com/paper/1901.11195/) (2019)

## Built on by (most cited first, 2 of 2)

- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [FSS-Net: Frequency-Spatial Synergy Network with Wavelet Attention for Carotid Artery Ultrasound Segmentation](https://mlsanity.com/paper/2606.10378/) (2026)

## Abstract (arXiv)

Parsing of eye components (i.e. pupil, iris and sclera) is fundamental for eye tracking and gaze estimation for AR/VR products. Mainstream approaches tackle this problem as a multi-class segmentation task, providing only visible part of pupil/iris, other methods regress elliptical parameters using human-annotated full pupil/iris parameters. In this paper, we consider two priors: projected full pupil/iris circle can be modelled with ellipses (ellipse prior), and the visibility of pupil/iris is controlled by openness of eye-region (condition prior), and design a novel method CondSeg to estimate elliptical parameters of pupil/iris directly from segmentation labels, without explicitly annotating full ellipses, and use eye-region mask to control the visibility of estimated pupil/iris ellipses. Conditioned segmentation loss is used to optimize the parameters by transforming parameterized ellipses into pixel-wise soft masks in a differentiable way. Our method is tested on public datasets (OpenEDS-2019/-2020) and shows competitive results on segmentation metrics, and provides accurate elliptical parameters for further applications of eye tracking simultaneously.