Context-Aware Fusion for Continuous Biometric Authentication

Abstract

Continuous authentication using biometrics is receiving renewed attention owing to recent advances in mobile technology. However, the context in which biometric inputs are acquired can affect the quality of information available for authentication. For example, in multi-speaker environments, face or gait could be better authenticators than voice. Unfortunately, existing fusion methods do not take this into account. In this paper, we propose a novel fusion method that accounts for context, and that can operate at both decision and score levels. Theoretical bounds on the proposed method are presented along with experiments on synthetic and real multi-modal biometric data. The results show that our proposed method is better than commonly used fusion methods, even when using state-of-the-art deep learners. Moreover, our method outperforms score-level fusion methods even at the decision-level, debunking the common myth that decision-level fusion is inferior, and showcasing the power of contextual learning.