Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures

Osman Tanyeli; Hüseyin Çakır

doi:10.47000/tjmcs.1826864

Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures

Abstract

Biometric systems enhance security using physiological and behavioral traits but face challenges like environmental factors and spoofing risks. Unimodal systems struggle with accuracy and reliability, prompting the adoption of biometric fusion. This strategy combines multiple modalities at different levels—sensor, feature, score, and decision—to improve robustness. This systematic review, following the PRISMA framework, analyzes 120 studies from 2000 to 2024 on fusion strategies for access control. It highlights performance advantages, key algorithms, and emerging trends, including deep learning, blockchain, and edge computing. The analysis demonstrates that multimodal systems significantly reduce error rates, such as achieving an EER of 0.85% compared to 2.5% in unimodal fingerprint systems. Furthermore, the review explores critical privacy-enhancing technologies and ethical concerns, such as data misuse and algorithmic bias, while proposing secure frameworks to mitigate these risks. The findings provide crucial insights for developing advanced, efficient, and secure biometric systems tailored for critical infrastructure protection. Future work should focus on explainable AI, standardization, and lightweight algorithms for broader adoption.

Keywords

References

Ahmed, M., Malik, T., Technological advancements in biometric fusion, Security and Privacy Journal, 2021.
Albrecht S., et al. Blockchain and biometrics: A survey on scalability, privacy, and security challenges, Journal of Network and Computer Applications 207(2022).
Bonneau, J., Herley, C., Van Oorschot, P.C., Stajano, F., The quest to replace passwords: A framework for comparative evaluation of web authentication schemes, IEEE Symposium on Security and Privacy,(2012).
Bowyer, K.W., Hollingsworth, K., Flynn P.J.. Image understanding for iris biometrics: A survey, Computer Vision and Image Understanding 110(2)(2008), 281–307.
Chen, Z., Zhang, Q., Challenges in raw data fusion for biometric systems, Journal of Computational Intelligence (2018).
Clarke, N.L., Furnell, S.M., Authentication of users on mobile telephones: A survey of attitudes and practices, Computers & Security 24(7)(2005), 519-527.
Das, R., Sengupta, S., Reducing error rates in multimodal biometric authentication, Biometric Authentication Journal, (2020).
Daugman, J.G., How iris recognition works, IEEE Transactions on Circuits and Systems for Video Technology 14(1)(2004), 21-30.

Edge, K., Martin, A., Real-time biometric authentication using edge computing. IoT and Cybersecurity Journal, (2020).
Frontex, Biometric Matching in Automated Border Control: A Practical Guide, Warsaw: European Border and Coast Guard Agency, 2022.
Gupta, N., Verma, S., Latency reduction in edge-based biometric systems, Journal of Real-Time Computing, (2023).
Hemdan, E.E.D., et al. Explainable AI (XAI) for biometrics: A comprehensive review of methods and challenges, ACM Computing Surveys, 56(3)(2023), 1-38.
Jain, A.K., Ross, A., Prabhakar, S., An introduction to biometric recognition, IEEE Transactions on Circuits and Systems for Video Technology 14(1)(2004), 4-20.
Kinnunen, T., Li, H., Voice biometrics: State-of-the-art, Speech Communication, (2010).
Kumar, A., Zhang, D., Score fusion methods in multimodal biometrics, IEEE Transactions on Systems, Man, and Cybernetics, 2012.
Lee, J., Chen, H., Combining iris and fingerprint features for enhanced accuracy. Cybersecurity Review, (2018).
Liu, F., Zhao, M., Comparative analysis of fusion methods in mobile biometric systems. Mobile Computing and Security Journal, (2019).
Maltoni, D., Maio, D., Handbook of Fingerprint Recognition, Springer, 2009.
Mendoza, C.V., Carbonell, M.A., A multi-modal biometric fusion approach for automated border control systems, IET Biometrics, 10(5)(2021), 516-530.
Nakamoto, S., Blockchain technology in secure systems, Cryptographic Innovations Journal, (2008).
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J., The FERET evaluation methodology for face-recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10)(2000), 1090-1104.
Phillips, P.J., Scruggs, W., Face recognition vendor test, NIST Reports, (2010).
Pournaghi, S.M., et al., A survey on blockchain-based identity management and access control for healthcare systems, Journal of Information Security and Applications, 55(2020), 102621.
Raghavendra, R., Busch, C., Majority voting strategies in biometric fusion, Biometric Authentication Journal, (2016).
Rattani, A., Ross, A., A fusion approach for improving matching accuracy of poor-quality fingerprints, Pattern Recognition Letters, (2010).
Ross, A., Jain, A.K., Information fusion in biometrics. Pattern Recognition Letters 24(13)(2003), 2115-2125.
Ross, A., Jain, A.K., Multimodal biometrics: An overview, Proceedings of 12th European Signal Processing Conference, (2004).
Sanderson, C., Paliwal, K.K., Information fusion and person verification using speech and face information. IDIAP-RR, 2003.
Sharma, R., Singh, V., Blockchain in national ID biometric systems, Global Privacy Review, 2022.
Smith, P., et al. User experience and performance analysis of biometric e-gates in air travel, Journal of Airport Management, 14(3)(2020), 254-268.
Sun, Y., Li, F., Advanced score aggregation techniques for biometric systems, Pattern Analysis and Applications, (2019).
Terhörst, P., et al., A comprehensive study on bias in deep learning networks for face recognition: From data to models, IEEE Transactions on Biometrics, Behavior, and Identity Science, 4(4)(2022), 496-513.
Uludag, U., Pankanti, S., Prabhakar, S., Jain, A.K., Biometric cryptosystems: Issues and challenges, Proceedings of the IEEE, 92(6)(2004), 948-960.
Wayman, J.L., Jain, A.K., Maltoni, D., Maio, D. (editors), Biometric Systems: Technology, Design and Performance Evaluation, Springer Science & Business Media, 2005.
Wilson, C., Sanchez, F., Fingerprint integration in multimodal systems, Journal of Biometrics Research, (2017).
Wu, Q., Zhang, J., Improving multimodal biometric systems with CNNs, Journal of Machine Learning Research, (2022).
Zhang, Y., Wang, Q., Score-level fusion techniques for robust biometric authentication, Pattern Recognition and Applications, (2017).
Zhou, J.,Wang, Y., A comprehensive review of sensor-level fusion in multimodal biometrics, IEEE Transactions on Information Forensics and Security, (2016). https://doi.org/10.1109/TIFS.2016.2585309

Details

Primary Language

English

Subjects

Data Security and Protection, Cybersecurity and Privacy (Other)

Journal Section

Research Article

Authors

Osman Tanyeli ^*
0009-0008-6987-5768
Cyprus

Hüseyin Çakır
0000-0001-9424-2323
Türkiye

Publication Date

June 30, 2026

Submission Date

November 20, 2025

Acceptance Date

January 12, 2026

Published in Issue

Year 2026 Volume: 18 Number: 2

DOI

https://doi.org/10.47000/tjmcs.1826864

IZ

https://izlik.org/JA93WC24NF

Cite

RIS / Bibtex

APA

Tanyeli, O., & Çakır, H. (2026). Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures. Turkish Journal of Mathematics and Computer Science, 18(2), 359-370. https://doi.org/10.47000/tjmcs.1826864

AMA

1.Tanyeli O, Çakır H. Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures. TJMCS. 2026;18(2):359-370. doi:10.47000/tjmcs.1826864

Chicago

Tanyeli, Osman, and Hüseyin Çakır. 2026. “Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures”. Turkish Journal of Mathematics and Computer Science 18 (2): 359-70. https://doi.org/10.47000/tjmcs.1826864.

EndNote

Tanyeli O, Çakır H (June 1, 2026) Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures. Turkish Journal of Mathematics and Computer Science 18 2 359–370.

IEEE

[1]O. Tanyeli and H. Çakır, “Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures”, TJMCS, vol. 18, no. 2, pp. 359–370, June 2026, doi: 10.47000/tjmcs.1826864.

ISNAD

Tanyeli, Osman - Çakır, Hüseyin. “Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures”. Turkish Journal of Mathematics and Computer Science 18/2 (June 1, 2026): 359-370. https://doi.org/10.47000/tjmcs.1826864.

JAMA

1.Tanyeli O, Çakır H. Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures. TJMCS. 2026;18:359–370.

MLA

Tanyeli, Osman, and Hüseyin Çakır. “Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures”. Turkish Journal of Mathematics and Computer Science, vol. 18, no. 2, June 2026, pp. 359-70, doi:10.47000/tjmcs.1826864.

Vancouver

1.Osman Tanyeli, Hüseyin Çakır. Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures. TJMCS. 2026 Jun. 1;18(2):359-70. doi:10.47000/tjmcs.1826864