# Eye Semantic Segmentation with a Lightweight Model

arXiv: https://arxiv.org/abs/1911.01049 · HTML: https://mlsanity.com/paper/1911.01049/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [Towards Unsupervised Eye-Region Segmentation for Eye Tracking](https://mlsanity.com/paper/2410.06131/) (2024)
- [Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning](https://mlsanity.com/paper/2206.09256/) (2022)

## Abstract (arXiv)

In this paper, we present a multi-class eye segmentation method that can run the hardware limitations for real-time inference. Our approach includes three major stages: get a grayscale image from the input, segment three distinct eye region with a deep network, and remove incorrect areas with heuristic filters. Our model based on the encoder decoder structure with the key is the depthwise convolution operation to reduce the computation cost. We experiment on OpenEDS, a large scale dataset of eye images captured by a head-mounted display with two synchronized eye facing cameras. We achieved the mean intersection over union (mIoU) of 94.85% with a model of size 0.4 megabytes. The source code are available https://github.com/th2l/Eye_VR_Segmentation