# Eyenet: Attention based Convolutional Encoder-Decoder Network for Eye Region Segmentation

arXiv: https://arxiv.org/abs/1910.03274 · HTML: https://mlsanity.com/paper/1910.03274/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking](https://mlsanity.com/paper/2205.01947/) (2022)
- [Generating Robust Adversarial Examples against Online Social Networks (OSNs)](https://mlsanity.com/paper/2310.12708/) (2023)
- [Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning](https://mlsanity.com/paper/2206.09256/) (2022)
- [Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025](https://mlsanity.com/paper/2508.10737/) (2025)
- [DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery](https://mlsanity.com/paper/2311.07235/) (2023)

## Abstract (arXiv)

With the immersive development in the field of augmented and virtual reality, accurate and speedy eye-tracking is required. Facebook Research has organized a challenge, named OpenEDS Semantic Segmentation challenge for per-pixel segmentation of the key eye regions: the sclera, the iris, the pupil, and everything else (background). There are two constraints set for the participants viz MIOU and the computational complexity of the model. More recently, researchers have achieved quite a good result using the convolutional neural networks (CNN) in segmenting eyeregions. However, the environmental challenges involved in this task such as low resolution, blur, unusual glint and, illumination, off-angles, off-axis, use of glasses and different color of iris region hinder the accuracy of segmentation. To address the challenges in eye segmentation, the present work proposes a robust and computationally efficient attention-based convolutional encoder-decoder network for segmenting all the eye regions. Our model, named EyeNet, includes modified residual units as the backbone, two types of attention blocks and multi-scale supervision for segmenting the aforesaid four eye regions. Our proposed model achieved a total score of 0.974(EDS Evaluation metric) on test data, which demonstrates superior results compared to the baseline methods.