Advancing Biometric Authentication With Dual-Threshold Multi-Modal Systems and Geometric Programming for Enhanced Digital Security

Source: IEEE Transactions on Dependable and Secure Computing, vol. 23, no. 1, pp. 1703-1719 (2026)

Authors: Frank Yeong-Sung Lin, Tzu-Lung Sun, Po-Chun Yu, Pin-Ruei Liu, Li-Min Zheng, Chiu-Han Hsiao

URL: https://doi.org/10.1109/TDSC.2025.3620382

Abstract: Biometric recognition plays an increasingly pivotal role in cybersecurity, where the CIA triad, Confidentiality, Integrity, and Availability, forms the cornerstone of information security, with authentication as a critical yet challenging component. This paper presents the Biometric Multi-modal Authentication System using Geometric Programming (BMMA-GPT), tailored for deployment in Fast IDentity Online (FIDO/FIDO2)-enabled environments and Zero Trust Architectures (ZTA). The system employs a dual-threshold mechanism integrated with Defense-in-Depth (DiD) strategies to simultaneously enhance accuracy, efficiency, and security. The underlying optimization problem is formulated as a mathematical programming task and reformulated into a Geometric Programming (GP) model to efficiently compute optimal biometric permutations and verification thresholds under constrained estimation errors. BMMA-GPT enables the flexible integration of multiple biometric modalities, allowing dynamic adjustments to meet both individual user profiles and organizational security requirements. It achieves a high Area Under Curve (AUC) of approximately 0.99 while maintaining authentication latency under 1.5 seconds. This design supports Chief Information Security Officers (CISOs) in configuring authentication processes tailored with minimal computational cost, enhancing resilience against spoofing attacks and ensuring a seamless user experience. By aligning the verification by LR with the DiD principles and GP-based optimization, the proposed framework offers a scalable and robust solution for identity authentication in complex digital ecosystems.