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.

翻译：持续身份认证通过持续监测和验证用户身份，为移动设备提供了高安全性与可用性，已得到广泛研究。近年研究采用多生物特征融合进行持续身份认证，即使部分捕获的生物特征数据质量较低，仍能实现高精度。然而，现有持续融合方法因依赖所有分类器始终处于激活状态而资源消耗大，可能不适用于移动设备。本文提出一种新的多生物特征持续认证方法：二维动态融合。其核心洞察在于，多生物特征持续认证需在分类器维度和时间维度上计算二维匹配分数。基于此，我们根据认证发生的上下文动态选择一组分类器，并通过多分类器融合与多样本融合对匹配分数进行整合。实验评估表明，相较于现有融合方法，本方法在资源消耗与精度之间实现了更优平衡。特别地，在相同分数计算次数下，通过采用多样本融合，本方法能提供比现有方法更高的认证精度。