International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 6 (June 2024), Pages: 89-96

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 Original Research Paper

High-accuracy models for iris recognition with merging features

 Author(s): 

 Hind Moutaz Al-Dabbas 1, *, Raghad Abdulaali Azeez 2, Akbas Ezaldeen Ali 3

 Affiliation(s):

 1Department of Computer Science, College of Education for Pure Science/Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq
 2Information Technology Unit, College of Education for Human Science-Ibn-Rushed, University of Baghdad, Baghdad, Iraq
 3Department of Computer Science, University of Technology, Baghdad, Iraq

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3912-2051

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.06.010

 Abstract

Due to advancements in computer science and technology, impersonation has become more common. Today, biometrics technology is widely used in various aspects of people's lives. Iris recognition, known for its high accuracy and speed, is a significant and challenging field of study. As a result, iris recognition technology and biometric systems are utilized for security in numerous applications, including human-computer interaction and surveillance systems. It is crucial to develop advanced models to combat impersonation crimes. This study proposes sophisticated artificial intelligence models with high accuracy and speed to eliminate these crimes. The models use linear discriminant analysis (LDA) for feature extraction and mutual information (MI), along with analysis of variance (ANOVA) for feature selection. Two iris classification systems were developed: one using LDA as an input for the OneR machine learning algorithm and another innovative hybrid model based on a One Dimensional Convolutional Neural Network (HM-1DCNN). The MMU database was employed, achieving a performance measure of 94.387% accuracy for the OneR model. Additionally, the HM-1DCNN model achieved 99.9% accuracy by integrating LDA with MI and ANOVA. Comparisons with previous studies show that the HM-1DCNN model performs exceptionally well, with at least 1.69% higher accuracy and lower processing time.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Iris recognition, Biometric security, Artificial intelligence, Feature selection, Impersonation prevention

 Article history

 Received 28 January 2024, Received in revised form 27 May 2024, Accepted 28 May 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Al-Dabbas HM, Azeez RA, and Ali AE (2024). High-accuracy models for iris recognition with merging features. International Journal of Advanced and Applied Sciences, 11(6): 89-96

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 

 Tables

 Table 1 Table 2 

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