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

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.