# PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection

arXiv: https://arxiv.org/abs/1711.00112 · HTML: https://mlsanity.com/paper/1711.00112/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 15 of 15)

- [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)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning](https://mlsanity.com/paper/2102.01921/) (2021)
- [EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking](https://mlsanity.com/paper/2205.01947/) (2022)
- [Maximum and Leaky Maximum Propagation](https://mlsanity.com/paper/2105.10277/) (2021)
- [Precise localization of corneal reflections in eye images using deep learning trained on synthetic data](https://mlsanity.com/paper/2304.05673/) (2023)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [Tensor Normalization and Full Distribution Training](https://mlsanity.com/paper/2109.02345/) (2021)
- [GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projection](https://mlsanity.com/paper/2201.07692/) (2022)
- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [Towards Everyday Virtual Reality through Eye Tracking](https://mlsanity.com/paper/2203.15703/) (2022)
- [An optical biomimetic eyes with interested object imaging](https://mlsanity.com/paper/2108.04236/) (2021)
- [EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR](https://mlsanity.com/paper/2507.09649/) (2025)
- [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)

## Abstract (arXiv)

Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in realworld scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non-centered and off-axis eye recording, as well as physiological eye characteristics. In this paper, we approach this challenge through: I) a convolutional neural network (CNN) running in real time on a single core, II) a novel computational intensive two stage CNN for accuracy improvement, and III) a fast propability distribution based refinement method as a practical alternative to II. We evaluate the proposed approaches against the state-of-the-art pupil detection algorithms, improving the detection rate up to ~9% percent points on average over all data sets (~7% on one CPU core 7ms). This evaluation was performed on over 135,000 images: 94,000 images from the literature, and 41,000 new hand-labeled and challenging images contributed by this work (v1.0).