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A novel scheme to address the fusion uncertainty in multi-modal continuous authentication schemes on mobile devices

机译:一种新颖的方案,用于解决移动设备多模态连续认证方案中的融合不确定性

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摘要

Interest in continuous mobile authentication schemes has increased in recent years. These schemes use sensors on mobile devices to collect the biometric data about a user. The use of multiple sensors in a multi-modal scheme has been shown to improve the accuracy. However, sensor scores are often combined using simplistic techniques such as averaging. To date, the effect of uncertainty in score fusion has not been explored. In this paper, we present a novel Dempster-Shafer based score fusion approach for continuous authentication schemes. Our approach combines the sensor scores factoring in the uncertainty of the sensor. We propose and evaluate five techniques for computing uncertainty. Our proof-of-concept system is tested on three state-of-the-art datasets and compared with common fusion techniques. We find that our proposed approach yields the highest accuracies compared to the other fusion techniques and achieves equal error rates as low as 8.05%.
机译:近年来,对持续移动认证方案的兴趣增加。这些方案在移动设备上使用传感器来收集有关用户的生物识别数据。已经显示了在多模态方案中使用多个传感器来提高精度。然而,传感器分数通常使用简单的技术(例如平均)组合。迄今为止,尚未探讨不确定性在分数融合中的影响。在本文中,我们提出了一种基于Dempster-Shafer基于连续认证方案的分数融合方法。我们的方法将传感器分数与传感器的不确定性相结合。我们提出并评估了五种计算不确定性的技术。我们的概念证据系统在三个最先进的数据集中进行测试,并与常见的融合技术进行比较。我们发现,与其他融合技术相比,我们所提出的方法能够获得最高的精度,并实现低至8.05%的相同误差率。

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