EchoLock: Towards Low-effort Mobile User Identification Leveraging Structure-borne Echos
EchoLock: Towards Low-effort Mobile User Identification Leveraging Structure-borne Echos
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DOI：
10.1145/3320269.3384741
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发表时间：
2020-03
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影响因子：
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通讯作者：
Yilin Yang;Chen Wang;Yingying Chen;Yan Wang
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作者：
Yilin Yang;Chen Wang;Yingying Chen;Yan Wang
许多现有的识别方法需要主动的用户输入、专门的传感硬件或个人身份信息，例如指纹或面部扫描。在本文中，我们提出了 EchoLock，这是一种省力的识别方案，可通过商用麦克风和扬声器感知手部几何形状来验证用户。 EchoLock 可以作为高端设备的补充验证方法，也可以作为低端设备的独立用户识别方案，而无需使用隐私敏感功能。除了安全应用程序之外，我们的系统还可以个性化用户与智能设备的交互，例如当不同的人持有智能遥控器时自动调整设置或偏好。为此，我们研究了手对移动设备中结构声传播的影响，并开发了一种用户识别方案，该方案可以测量、量化和利用不同的声音反射来区分不同的身份。特别是，我们提出了一种非侵入式手部传感技术，可以在时域和频域中获得独特的声学特征，可以有效地捕获用户手部的生理和行为特征（例如，手部轮廓、手指尺寸、握持力度和握持风格）。此外，还开发了基于学习的算法，以在各种环境和条件下稳健地识别用户。我们对 20 名参与者进行了广泛的实验，在 160 个关键用例场景中使用不同的硬件设置收集了 80,000 个手部几何样本。我们的结果表明，EchoLock 能够以超过 94% 的准确率识别用户，而不需要任何主动的用户输入。
Many existing identification approaches require active user input, specialized sensing hardware, or personally identifiable information such as fingerprints or face scans. In this paper, we propose EchoLock, a low-effort identification scheme that validates the user by sensing hand geometry via commodity microphones and speakers. EchoLock can serve as a complementary verification method for high-end devices or as a stand-alone user identification scheme for lower-end devices without using privacy-sensitive features. In addition to security applications, our system can also personalize user interactions with smart devices, such as automatically adapting settings or preferences when different people are holding smart remotes. To this end, we study the impact of hands on structure borne sound propagation in mobile devices and develop a user identification scheme that can measure, quantify, and exploit distinct sound reflections in order to differentiate distinct identities. Particularly, we propose a non-intrusive hand sensing technique to derive unique acoustic features in both time and frequency domain, which can effectively capture the physiological and behavioral traits of a user's hand (e.g., hand contours, finger sizes, holding strengths, and holding styles). Furthermore, learning-based algorithms are developed to robustly identify the user under various environments and conditions. We conduct extensive experiments with 20 participants, gathering 80,000 hand geometry samples using different hardware setups across 160 key use case scenarios. Our results show that EchoLock is capable of identifying users with over 94% accuracy, without requiring any active user input.