Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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A Segmentation Scheme For Robust Iris Based On Improved U-Net

Authors
B. P. N. Madhu Kumar1, *, V. G. Bhavani1, N. Ch. S. Prasad1, G. P. Kumar1, G. Roop Kumar1, K. Shanmukh1
1Department of CSE, BVC Engineering College, Odalarevu, India
*Corresponding author. Email: bpnmadhukumar@gmail.com
Corresponding Author
B. P. N. Madhu Kumar
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_92How to use a DOI?
Keywords
FD-UNet; Iris segmentation; UBIRIS.v2; CASIA-iris-interval-v4.0; ND-IRIS-0405
Abstract

The reliance of the iris recognition system on high-quality iris segmentation creates a strong foundation for future iris recognition research and greatly improves the efficiency of iris identification. By using the same datasets for training and testing, we were able to obtain the top network model, FD-UNet. This network architecture combines elements from U-Net and four others that have proven effective. By switching from original convolution to dilated convolution, the FD-UNet improves picture processing by extracting more global features. Datasets such as UBIRIS.v2 for visible light illumination, CASIA-iris-interval-v4.0 and ND-IRIS-0405 for near-infrared illumination, and others were utilised to evaluate the proposed method. Our model hit f1 scores of 97.36%, 96.74%, and 94.81% on the CASIA-iris-interval-v4.0, ND-IRIS-0405, and UBIRIS.v2 datasets, respectively. Our network model outperforms the competition with a reduced error rate, according to the trial data. It is quite reliable and performs admirably with iris datasets that use both visible light and near-infrared lighting.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_92
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_92How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. P. N. Madhu Kumar
AU  - V. G. Bhavani
AU  - N. Ch. S. Prasad
AU  - G. P. Kumar
AU  - G. Roop Kumar
AU  - K. Shanmukh
PY  - 2024
DA  - 2024/07/30
TI  - A Segmentation Scheme For Robust Iris Based On Improved U-Net
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 962
EP  - 968
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_92
DO  - 10.2991/978-94-6463-471-6_92
ID  - Kumar2024
ER  -