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SEGAUTH: A segment-based approach to behavioral biometric authentication

机译：SEGAUTH：基于段的行为生物特征认证方法

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Many studies have been conducted to apply behavioral biometric authentication on/with mobile devices and they have shown promising results. However, the concern about the verification accuracy of behavioral biometrics is still common given the dynamic nature of behavioral biometrics. In this paper, we address the accuracy concern from a new perspective- behavior segments, that is, segments of a gesture instead of the whole gesture as the basic building block for behavioral biometric authentication. With this unique perspective, we propose a new behavioral biometric authentication method called SEGAUTH, which can be applied to various gesture or motion based authentication scenarios. SEGAUTH can achieve high accuracy by focusing on each user's distinctive gesture segments that frequently appear across his or her gestures. In SEGAUTH, a time series derived from a gesture/motion is first partitioned into segments and then transformed into a set of string tokens in which the tokens representing distinctive, repetitive segments are associated with higher genuine probabilities than those tokens that are common across users. An overall genuine score calculated from all the tokens derived from a gesture is used to determine the user's authenticity. We have assessed the effectiveness of SEGAUTH using 4 different datasets. Our experimental results demonstrate that SEGAUTH can achieve higher accuracy consistently than existing popular methods on the evaluation datasets.
机译：已经进行了许多研究以在移动设备上/与移动设备一起应用行为生物特征认证，并且它们显示出令人鼓舞的结果。但是，考虑到行为生物特征的动态性质，对行为生物特征的验证准确性的关注仍然很普遍。在本文中，我们从一个新的角度解决了精度问题，即行为段，即手势的一部分而不是整个手势，作为行为生物特征认证的基本组成部分。以此独特的视角，我们提出了一种称为SEGAUTH的新的行为生物特征认证方法，该方法可以应用于各种基于手势或动作的认证方案。 SEGAUTH可以通过专注于每个用户的手势中经常出现的与众不同的手势段来实现高精度。在SEGAUTH中，首先将从手势/动作中获得的时间序列划分为多个段，然后转换为一组字符串标记，其中与众不同的重复标记相比，代表不同重复段的标记具有更高的真实概率。根据从手势得出的所有令牌计算出的总真实分数可用于确定用户的真实性。我们使用4个不同的数据集评估了SEGAUTH的有效性。我们的实验结果表明，SEGAUTH可以比评估数据集上现有的流行方法始终如一地获得更高的准确性。

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