<abstract xmlns="http://www.w3.org/1999/xhtml">

The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was performed using two databases which contain IR images with a spatial resolution of 640x480 pixels. The first database was acquired in our laboratory and contains 400 eye images and the second database is a selection of 400 images from the publicly available CASIA Iris Lamp database. The results obtained by applying the segmentation based on the CNN architecture were compared to manually-annotated ground truth data. The results obtained are comparable to the state of the art.
The purpose of the paper is to present the implementation of a robust segmentation algorithm based on the U-Net convolutional neural network that can be used in eye tracking applications such as human computer interface, communication devices for people with disabilities, marketing research or clinical studies.
The proposed method improves uppon existing U-Net CNN architectures in terms of efficiency, by reducing the total number of parameters used from 31 millions to 38k. The advantages of using a number of parameters approximatly 815 times lower than the original U-Net CNN architecture are reduced computing resources consumption and a lower inference time.
</abstract>

The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was performed using two databases which contain IR images with a spatial resolution of 640x480 pixels. The first database was acquired in our laboratory and contains 400 eye images and the second database is a selection of 400 images from the publicly available CASIA Iris Lamp database. The results obtained by applying the segmentation based on the CNN architecture were compared to manually-annotated ground truth data. The results obtained are comparable to the state of the art.
The purpose of the paper is to present the implementation of a robust segmentation algorithm based on the U-Net convolutional neural network that can be used in eye tracking applications such as human computer interface, communication devices for people with disabilities, marketing research or clinical studies.
The proposed method improves uppon existing U-Net CNN architectures in terms of efficiency, by reducing the total number of parameters used from 31 millions to 38k. The advantages of using a number of parameters approximatly 815 times lower than the original U-Net CNN architecture are reduced computing resources consumption and a lower inference time.

Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture

“Gheorghe Asachi” Technical University of Iași

“Politehnica” University of Bucharest

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture"}

The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was...

Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture

Published Online: Mar 12, 2022

Page range: 41 - 52

Received: Mar 29, 2021

Accepted: May 16, 2021

DOI: https://doi.org/10.2478/bipie-2021-0010

Keywords
deep learning, segmentation, eye dataset, U-Net, convolutional neural networks

© 2021 Casian Miron et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture

Published Online: Mar 12, 2022

Page range: 41 - 52

Received: Mar 29, 2021

Accepted: May 16, 2021

DOI: https://doi.org/10.2478/bipie-2021-0010

Keywordsdeep learning, segmentation, eye dataset, U-Net, convolutional neural networks

© 2021 Casian Miron et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
deep learning, segmentation, eye dataset, U-Net, convolutional neural networks