Two-Dimensional Dynamic Fusion for Continuous Authentication

TL;DR

This paper introduces a two-dimensional dynamic fusion method for continuous biometric authentication on mobile devices, optimizing resource use while maintaining high accuracy by dynamically selecting classifiers based on context.

Contribution

It proposes a novel two-dimensional dynamic fusion approach that adaptively selects classifiers over time and context, improving efficiency and accuracy in mobile continuous authentication.

Findings

Higher accuracy with same resource usage compared to existing methods

Better balance between resource consumption and authentication accuracy

Effective multi-sample fusion enhances performance

Abstract

Continuous authentication has been widely studied to provide high security and usability for mobile devices by continuously monitoring and authenticating users. Recent studies adopt multibiometric fusion for continuous authentication to provide high accuracy even when some of captured biometric data are of a low quality. However, existing continuous fusion approaches are resource-heavy as they rely on all classifiers being activated all the time and may not be suitable for mobile devices. In this paper, we propose a new approach to multibiometric continuous authentication: two-dimensional dynamic fusion. Our key insight is that multibiometric continuous authentication calculates two-dimensional matching scores over classifiers and over time. Based on this, we dynamically select a set of classifiers based on the context in which authentication is taking place, and fuse matching scores by multi-classifier fusion and multi-sample fusion. Through experimental evaluation, we show that our approach provides a better balance between resource usage and accuracy than the existing fusion methods. In particular, we show that our approach provides higher accuracy than the existing methods with the same number of score calculations by adopting multi-sample fusion.