论文标题
有心脏：减轻心脏信号对生物特征识别的影响
CardioID: Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification
论文作者
论文摘要
心脏模式被用于获得难以伪造的生物特征特征，并导致了最新的（SOA）识别应用程序的准确性。但是，这种性能是在心脏信号保持相对均匀模式的受控场景下获得的，从而促进了识别过程。在这项工作中，我们分析了在更现实（不受控制的）场景中收集的心脏信号，并表明它们的高信号变异性（即不规则性）使得获得稳定且独特的用户功能变得更加困难。此外，SOA通常无法识别特定的用户组，从而使现有的识别方法在不受控制的方案中徒劳无功。为了解决这些问题，我们提出了一个具有三个新型特性的框架。首先，我们设计了一种自适应方法，该方法通过为每个用户定制过滤频谱来实现稳定且独特的功能。其次，我们表明用户可以具有多种心脏形态，与SOA相比，我们为我们提供了更大的心脏信号和用户。第三，我们克服了使用多群集方法和Mahalanobis距离的身份验证应用中存在的其他失真效应。我们的评估表明，在不受控制的方案中，SOA的平均平衡精度（BAC）从受控方案中的90％下降到75％，而我们的方法在不受控制的场景中平均BAC以上的平均BAC以上90％以上。
Cardiac patterns are being used to obtain hard-to-forge biometric signatures and have led to high accuracy in state-of-the-art (SoA) identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal variability (i.e., irregularity) makes it harder to obtain stable and distinct user features. Furthermore, SoA usually fails to identify specific groups of users, rendering existing identification methods futile in uncontrolled scenarios. To solve these problems, we propose a framework with three novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering spectrum to each user. Second, we show that users can have multiple cardiac morphologies, offering us a much bigger pool of cardiac signals and users compared to SoA. Third, we overcome other distortion effects present in authentication applications with a multi-cluster approach and the Mahalanobis distance. Our evaluation shows that the average balanced accuracy (BAC) of SoA drops from above 90% in controlled scenarios to 75% in uncontrolled ones, while our method maintains an average BAC above 90% in uncontrolled scenarios.
