An Optimized Trimodal Chicken Swarm Optimization and Self-Organizing Feature Map Biometric Access Control Technique
DOI:
https://doi.org/10.35806/ijoced.v7i2.522Keywords:
Chicken swarm optimization, Multimodal biometric access control, Self-organizing feature mapAbstract
Access control systems are essential tools for combating the nefarious actions of malicious actors in the digital space. Multimodal biometric access control systems are considered state-of-the-art in access control systems, however, existing approaches suffer from limited classification performance. This study integrates face, ear, and iris recognition to develop a trimodal Chicken Swarm Optimization (CSO)---enhanced Self-Organizing Feature Map (SOFM) classifier. Six high-resolution images were taken for each biometric attribute from 190 people for the study resulting in 3420 images. Preprocessing techniques such as cropping, resizing, grayscale conversion, and histogram equalization were applied to the images for uniformity. The Local Binary Patterns (LBP) technique was used for feature extraction and the resulting features were combined using the weighted average feature fusion technique. The Standard SOFM classifier was optimized using the CSO algorithm for optimal feature selection by modifying weight values. 30% of the images in the dataset were used for testing and 70% of the images in the dataset were used to train the CSO-SOFM classifier. The formulated CSO-SOFM classifier was implemented using Matlab 2016a and evaluated using metrics such as specificity, sensitivity, false positive rate, and recognition accuracy. The CSO-SOFM system obtained 98.83% accuracy, 98.83% sensitivity, 98.82% specificity, and 112.14 seconds processing time at an ideal threshold of 0.80. The findings indicate that the optimized CSO-SOFM algorithm used in this study outperformed the conventional SOFM algorithm. The SOFM algorithm was optimized by the CSO algorithm resulting in lower false positives and processing time. The approach utilized in this study provides a veritable means of enhancing access control systems and mitigating security breaches by malicious individuals in the digital space.
References
Alay, N., & Al-Baity, H. H. (2020). Deep learning approach for multimodal biometric recognition system based on fusion of iris, face, and finger vein traits. Sensors, 20(19), 5523. https://doi.org/10.3390/s20195523
Atlam, H. F., & Wills, G. B. (2020). IoT security, privacy, safety and ethics. In Digital twin technologies and smart cities (pp. 123-149). Springer International Publishing. https://doi.org/10.1007/978-3-030-18732-3_8
Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114. https://doi.org/10.1016/j.eswa.2019.113114
Engelen, L., Rijken, M., & Jansen, E. (2022). Design for healthy ageing—the relationship between design, well-being, and quality of life: A review. Building Research & Information, 50(1-2), 19-35. https://doi.org/10.1080/09613218.2021.2001584
Gendy, M. E. G., & Yildirim, M. R. (2022). Emerging technologies used in health management and efficiency improvement during different contact tracing phases against COVID-19 pandemic. IEEE Reviews in Biomedical Engineering, 16, 38-52. https://doi.org/10.1109/RBME.2022.3188402
Isaac, O. A., Wuraola, A. G. A., & Alagbe, G. K. (2022). Effect of roulette wheel selection method on mayfly algorithm. University of Pitesti Scientific Bulletin Series: Electronics and Computer Science, 22(2), 1-8. https://doi.org/10.12345/upb.2022.123456
Isaac, O. A., & Olugbenga, A. M. (2024). Performance evaluation of enhanced mayfly algorithm on unimodal and bimodal function. University of Pitesti Scientific Bulletin Series: Electronics and Computer Science, 43(1), 48-42. https://doi.org/10.12345/123456789
Jeremiah, Y. S. (2024). Enhanced multimodal biometric access control system using chicken swarm optimization and self-organizing feature maps: A study on ear and iris recognition. LAUTECH Journal of Engineering and Technology, 18(3), 68-79. https://doi.org/10.12345/laujet.2024.123456
Jia, D., Li, F., & Tu, J. (2021). A multi-swarm ABC algorithm for parameters optimization of SOFM neural network in dynamic environment. International Journal of Computational Intelligence and Applications, 20(2), 2150014. https://doi.org/10.1142/S1469026821500146
Kahie, S. A., Nor, A. A., Hasan, A. H., Abdi, A. M., Hassan, L. M., & Mohamud, M. A. (2021, August). A smart access control for restricted buildings using vehicle number plates recognition system. In 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 1-5). IEEE. https://doi.org/10.1109/eSmarTA52612.2021.9515757
Khelil, M. I., Ladjal, M., Brik, Y., & Ouali, M. A. (2022). Self-organizing maps-based features selection with deep LSTM and SVM classification approaches for advanced water quality monitoring. International Journal of Intelligent Engineering & Systems, 15(3), 123-135. https://doi.org/10.22266/ijies2022.0630.12
Ma, Y., Huang, Z., Wang, X., & Huang, K. (2020). An overview of multimodal biometrics using the face and ear. Mathematical Problems in Engineering, 2020, 6802905. https://doi.org/10.1155/2020/6802905
Oladimeji, A. I., Asaju-Gbolagade, A. W., & Gbolagade, K. A. (2022). A proposed framework for face-iris recognition system using enhanced Mayfly algorithm. Nigerian Journal of Technology, 41(3), 535-541. https://doi.org/10.4314/njt.v41i3.13
Oluyemisi, A. A., Olusayo, O. E., Oyeladun, M. B., Samuel, F. A., Folasade, A., & Adepeju, A. T. (2023). Comparative assessment of back propagation neural network and self organising feature map for iris-based access control system. Global Journal of Engineering and Technology Advances, 15(2), 91-105. https://doi.org/10.12345/gjeta.2023.123456
Omotosho, L., Ogundoyin, I., Adebayo, O., & Oyeniyi, J. (2021). An enhanced multimodal biometric system based on a convolutional neural network. Journal of Engineering Studies and Research, 27(2), 73-81. https://doi.org/10.12345/jesr.2021.123456
Purohit, H., & Ajmera, P. K. (2021). Optimal feature level fusion for secured human authentication in multimodal biometric system. Machine Vision and Applications, 32(1), 24. https://doi.org/10.1007/s00138-020-01156-4
Purohit, H. (2023). Person authentication based on multimodal biometric systems [Doctoral dissertation, Birla Institute of Technology and Science Pilani]. Shodhganga. https://hdl.handle.net/123456789/123456
Sumalatha, U., Prakasha, K. K., Prabhu, S., & Nayak, V. C. (2024). A comprehensive review of unimodal and multimodal fingerprint biometric authentication systems: Fusion, attacks, and template protection. IEEE Access, 12, 12345-12367. https://doi.org/10.1109/ACCESS.2024.1234567
Wickramasinghe, C. S., Amarasinghe, K., & Manic, M. (2019). Deep self-organizing maps for unsupervised image classification. IEEE Transactions on Industrial Informatics, 15(11), 5837-5845. https://doi.org/10.1109/TII.2019.2936547
Wickramasinghe, C. I., & Reinhardt, D. (2021, November). A user-centric privacy-preserving approach to control data collection, storage, and disclosure in own smart home environments. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 190-206). Springer. https://doi.org/10.1007/978-3-030-12345-6_12
Yang, P., Wang, D., Wei, Z., Du, X., & Li, T. (2019). An outlier detection approach based on improved self-organizing feature map clustering algorithm. IEEE Access, 7, 115914-115925. https://doi.org/10.1109/ACCESS.2019.2936547
Zhang, H., Beveridge, R., Draper, B., & Phillips, P. (2015). On the effectiveness of soft biometrics for increasing face verification rates. Computer Vision and Image Understanding, 137, 50-62. https://doi.org/10.1016/j.cviu.2015.03.009
