Iris Segmentation based on an Optimized U-Net

Sabry Abdalla M., Lubos Omelina, Lubos Omelina, Jan Cornelis, Bart Jansen, Bart Jansen

2022

Abstract

Segmenting images of the human eye is a critical step in several tasks like iris recognition, eye tracking or pupil tracking. There are a lot of well-established hand-crafted methods that have been used in commercial practice. However, with the advances in deep learning, several deep network approaches outperform the handcrafted methods. Many of the approaches adapt the U-Net architecture for the segmentation task. In this paper we propose some simple and effective new modifications of U-Net, e.g. the increase in size of convolutional kernels, which can improve the segmentation results compared to the original U-Net design. Using these modifications, we show that we can reach state-of-the-art performance using less model parameters. We describe our motivation for the changes in the architecture, inspired mostly by the hand-crafted methods and basic image processing principles and finally we show that our optimized model slightly outperforms the original U-Net and the other state-of-the-art models.

Paper Citation

in Harvard Style

M. S., Omelina L., Cornelis J. and Jansen B. (2022). Iris Segmentation based on an Optimized U-Net. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS; ISBN 978-989-758-552-4, SciTePress, pages 176-183. DOI: 10.5220/0010825800003123

in Bibtex Style

@conference{biosignals22,
author={Sabry Abdalla M. and Lubos Omelina and Jan Cornelis and Bart Jansen},
title={Iris Segmentation based on an Optimized U-Net},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS},
year={2022},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010825800003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS
TI - Iris Segmentation based on an Optimized U-Net
SN - 978-989-758-552-4
AU - M. S.
AU - Omelina L.
AU - Cornelis J.
AU - Jansen B.
PY - 2022
SP - 176
EP - 183
DO - 10.5220/0010825800003123
PB - SciTePress