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An Improved Chicken Swarm Optimization Techniques Based on Cultural Algorithm Operators for Biometric Access Control

Jonathan Ponmile Oguntoye1, Sunday Adeola Ajagbe2,3,*, Oluyinka Titilayo Adedeji1, Olufemi Olayanju Awodoye1, Abigail Bola Adetunji1, Elijah Olusayo Omidiora1, Matthew Olusegun Adigun2

1 Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, 210214, Nigeria
2 Department of Computer Science, University of Zululand, Kwadlangezwa, 3886, South Africa
3 Department of Computer Engineering, Abiola Ajimobi Technical University, Ibadan, 200255, Nigeria

* Corresponding Author: Sunday Adeola Ajagbe. Email: email

Computers, Materials & Continua 2025, 84(3), 5713-5732. https://doi.org/10.32604/cmc.2025.062440

Abstract

This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization (CCSO) technique. This approach mitigates the limitations of conventional Chicken Swarm Optimization (CSO), especially in dealing with larger dimensions due to diversity loss during solution space exploration. Our experimentation involved 600 sample images encompassing facial, iris, and fingerprint data, collected from 200 students at Ladoke Akintola University of Technology (LAUTECH), Ogbomoso. The results demonstrate the remarkable effectiveness of CCSO, yielding accuracy rates of 90.42%, 91.67%, and 91.25% within 54.77, 27.35, and 113.92 s for facial, fingerprint, and iris biometrics, respectively. These outcomes significantly outperform those achieved by the conventional CSO technique, which produced accuracy rates of 82.92%, 86.25%, and 84.58% at 92.57, 63.96, and 163.94 s for the same biometric modalities. The study’s findings reveal that CCSO, through its integration of Cultural Algorithm (CA) Operators into CSO, not only enhances algorithm performance, exhibiting computational efficiency and superior accuracy, but also carries broader implications beyond biometric systems. This innovation offers practical benefits in terms of security enhancement, operational efficiency, and adaptability across diverse user populations, shaping more effective and resource-efficient access control systems with real-world applicability.

Keywords

Access control; biometric technology; chicken swarm optimization; cultural algorithm; pattern recognition

Cite This Article

APA Style
Oguntoye, J.P., Ajagbe, S.A., Adedeji, O.T., Awodoye, O.O., Adetunji, A.B. et al. (2025). An Improved Chicken Swarm Optimization Techniques Based on Cultural Algorithm Operators for Biometric Access Control. Computers, Materials & Continua, 84(3), 5713–5732. https://doi.org/10.32604/cmc.2025.062440
Vancouver Style
Oguntoye JP, Ajagbe SA, Adedeji OT, Awodoye OO, Adetunji AB, Omidiora EO, et al. An Improved Chicken Swarm Optimization Techniques Based on Cultural Algorithm Operators for Biometric Access Control. Comput Mater Contin. 2025;84(3):5713–5732. https://doi.org/10.32604/cmc.2025.062440
IEEE Style
J. P. Oguntoye et al., “An Improved Chicken Swarm Optimization Techniques Based on Cultural Algorithm Operators for Biometric Access Control,” Comput. Mater. Contin., vol. 84, no. 3, pp. 5713–5732, 2025. https://doi.org/10.32604/cmc.2025.062440



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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