AI-Driven Biometric Authentication in IoT Systems: A Survey on Trade-Offs, Modalities, and Comparative Performance With Non-AI Approaches

Abstract

Biometric authentication has emerged as a critical security mechanism for IoT ecosystems, balancing usability and robustness against evolving threats. This survey comprehensively analyzes 24 peer-reviewed papers to evaluate the role of artificial intelligence (AI) in enhancing biometric authentication for IoT systems, contrasting its performance with traditional non-AI approaches. We categorize frameworks by biometric modality (fingerprint, facial recognition, ECG), IoT layer (edge, application), and security mechanisms (cryptography, multifactor authentication), with a focus on accuracy, latency, and cost tradeoffs. Key findings reveal that 42 % of systems leverage AI, achieving superior accuracy (for example 99.99% for facial recognition in AI-driven systems vs. 93.05 % in non-AI) but often incurring higher latency (for example 12s for AI-based HD-sEMG vs. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 5 0 2 ~ m s}$</tex> for non-AI Rabin cryptosystems). Non-AI systems dominate in low-resource environments (for example edge-layer fingerprint recognition) through lightweight cryptography (AES, Shamir Secret Sharing) and fuzzy extractors, prioritizing cost efficiency and real-time performance. AI excels in dynamic threat detection (for example honey templates with automated alarms) and complex biometrics (ECG, gait), though computational overhead remains a barrier for resource-constrained devices. We identify critical gaps, including the lack of standardized benchmarks for accuracy/latency and limited adoption of continuous authentication. This survey uniquely focuses on the comparative analysis of AI and non-AI biometric systems across different IoT layers and resource constraints-an angle underexplored in prior surveys This survey provides a practical insight for researchers and developers to navigate trade-offs between AI-driven innovation and traditional security in IoT biometrics.