Biometric Fusion Strategies for Enhancing Access Control in Critical Infrastructures

Biometric systems enhance security using physiological and behavioral traits but face challenges likeenvironmental factors and spoofing risks. Unimodal systems struggle with accuracy and reliability, prompting theadoption 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 120studies 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 demonstratesthat multimodal systems significantly reduce error rates, such as achieving an EER of 0.85% compared to 2.5% inunimodal fingerprint systems. Furthermore, the review explores critical privacy-enhancing technologies and ethicalconcerns, 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 forcritical infrastructure protection. Future work should focus on explainable AI, standardization, and lightweightalgorithms for broader adoption.