Adaptive Thresholding for Fair and Robust Biometric Authentication.

Thresholds play a crucial role in state-of-the-art biometric authentication systems as they determine the level of confidence required for a match between the presented biometric sample (e.g., facial image) and the stored reference template. These thresholds help balance security and convenience in biometric authentication. They are typically determined a priori based on some test data, and then fixed during system operation after deployment, and hence the same for all users. In this doctoral research, we investigate a.o. attacks against static thresholds by exploiting the non-uniform distributions of biometric characteristics, and research an extensible middleware solution for adaptive thresholding to offer the same level of security despite individual and demographic differences in biometric modalities. The ultimate goal of this study is to adjust state-of-the-art solutions of biometric authentication systems to dynamically adapt the threshold for enhanced security, robustness, and fairness. It goes without saying that incorporating dynamic adaptation within a distributed deployment environment introduces a new potential point of vulnerability in the attack surface. The projected middleware must duly address this critical issue.