CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
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DOI：
10.1145/3307334.3326093
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Jian Liu;Cong Shi;Yingying Chen;Hongbo Liu;M. Gruteser
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文献类型：
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作者：
Jian Liu;Cong Shi;Yingying Chen;Hongbo Liu;M. Gruteser
随着移动和物联网设备(如智能手机、平板电脑、智能家电)的日益普及，这些设备上存储了大量的隐私和敏感信息。为了防止对这些设备的未经授权的访问，现有的用户验证解决方案要么依赖于复杂的用户定义的秘密(例如密码)，要么求助于专门的生物特征传感器(例如指纹识别器)，但用户仍然可能遭受各种攻击，如密码被盗、肩部冲浪、污点和伪造的生物特征攻击。在本文中，我们提出了一种低成本、通用、难以伪造的用户验证系统，该系统利用了从移动设备和物联网设备中随时可用的内置摄像头提取的独特的心脏生物特征。我们演示了通过按压内置摄像头，可以从指尖的心脏运动模式中提取出独特的心脏特征。为了减轻实际场景中各种环境照明条件和人体运动的影响，MedicoCam开发了一种基于梯度的技术来优化相机配置，并动态选择相机帧中最敏感的像素来提取可靠的心脏运动模式。此外，利用形态特征分析提取特定于用户的心脏特征，并提出了一种基于主成分分析(PCA)的特征转换方案，以增强心脏生物特征识别的稳健性，从而实现有效的用户验证。在原型系统中，对$25$受试者进行了大量的实验，结果表明，在保持低至$4%$的假阳性率(FPR)的同时，该系统可以实现有效和可靠的用户验证，平均真阳性率(TPR)超过$99%。
With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving $25$ subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over $99%$ average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as $4%$.