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首页> 外文会议>International conference on human-computer interaction;International conference on distributed, ambient and pervasive interactions >A Look at Feet: Recognizing Tailgating via Capacitive Sensing
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A Look at Feet: Recognizing Tailgating via Capacitive Sensing

机译：看脚：通过电容感应识别尾语

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At many every day places, the ability to be reliably able to determine how many individuals are within an automated access control area, is of great importance. Especially in high-security areas such as banks and at country borders, access systems like mantraps or drop-arm turnstiles serve this purpose. These automated systems are designed to ensure that only one person can pass through a particular transit area at a time. State of the art systems use camera systems mounted in the ceiling to detect people sneaking in behind authorized individuals to pass through the transit space (tailgating attacks). Our novel method is inspired by recently achieved results in capacitive in-door-localization. Instead of estimating the position of humans, the pervasive capacitance of feet in the transit space is measured to detect tailgating attacks. We explore suitable sensing techniques and sensor-grid layout to be used for that application. In contrast to existing work, we use machine learning techniques for classification of the sensor's feature vector. The performance is evaluated on hardware-level, by defining its physical effectiveness. Tests with simulated attacks show its performance in comparison with competitive camera-image methods. Our method provides verification of tailgating attacks with an equal-error-rate of 3.5%, which outperforms other methods. We conclude with an evaluation of the amount of data needed for classification and highlight the usefulness of this method when combined with other imaging techniques.
机译：在每天的许多地方，可靠地确定自动访问控制区域内有多少个人的能力非常重要。特别是在银行等国家和边境地区的高安全性地区，门禁系统（如安全带或落臂式旋转门）可达到此目的。这些自动化系统旨在确保一次只能有一个人通过特定的运输区域。最先进的系统使用安装在天花板上的摄像头系统检测偷偷溜入经过授权人员身后以穿越运输空间（尾随攻击）的人员。我们的新颖方法受到最近在电容式室内定位中取得的成果的启发。而不是估计人类的位置，而是测量过境空间中脚的普遍电容来检测拖尾攻击。我们探索了适合该应用的合适的传感技术和传感器网格布局。与现有工作相反，我们使用机器学习技术对传感器的特征向量进行分类。通过定义其物理有效性，可以在硬件级别评估性能。与竞争对手的相机图像方法相比，使用模拟攻击进行的测试表明其性能。我们的方法以3.5％的均等错误率验证了尾随攻击，该方法优于其他方法。最后，我们对分类所需的数据量进行了评估，并强调了与其他成像技术结合使用时该方法的实用性。

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