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DOI: 10.14569/IJACSA.2020.0110742
PDF

Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition

Author 1: Eko Ihsanto
Author 2: Jeffry Kurniawan
Author 3: Diyanatul Husna
Author 4: Alfan Presekal
Author 5: Kalamullah Ramli

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

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Abstract: Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate.

Keywords: Iris recognition; iris segmentation; Zeta; authentication; biometric; pattern recognition

Eko Ihsanto, Jeffry Kurniawan, Diyanatul Husna, Alfan Presekal and Kalamullah Ramli, “Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110742

@article{Ihsanto2020,
title = {Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110742},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110742},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Eko Ihsanto and Jeffry Kurniawan and Diyanatul Husna and Alfan Presekal and Kalamullah Ramli}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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