MU-ID: Multi-user Identification Through Gaits Using Millimeter Wave Radios
MU-ID: Multi-user Identification Through Gaits Using Millimeter Wave Radios
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DOI：
10.1109/infocom41043.2020.9155471
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
X. Yang;Jian Liu;Yingying Chen;Xiaonan Guo;Yucheng Xie
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文献类型：
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作者：
X. Yang;Jian Liu;Yingying Chen;Xiaonan Guo;Yucheng Xie
多用户识别可以促进各种大规模的基于身份的服务，例如访问控制、自动监控系统和个性化服务等。虽然现有的解决方案可以使用摄像头识别多个用户，但这种基于视觉的方法通常会引起严重的隐私问题，并且需要视线的存在。不同的是，在本文中，我们提出了 MU-ID，这是一种基于步态的多用户识别系统，利用单个商用现成 (COTS) 毫米波 (mmWave) 雷达。特别地，MU-ID将来自雷达传感器的调频连续波(FMCW)信号作为输入。通过分析距离多普勒域中的毫米波信号，MU-ID 检查用户的下肢运动，并捕获其在步长、持续时间、瞬时下肢速度和下肢间距离等方面变化的独特步态模式。此外，还提出了有效的时空轮廓分析来分割每个用户的步行步骤。然后，系统使用卷积神经网络 (CNN) 分类器识别步骤，并进一步识别感兴趣区域中的用户。我们使用 TI AWR1642BOOST 毫米波传感器实施 MU-ID，并进行了涉及 10 人的广泛实验。结果表明，MU-ID的单人识别准确率高达97%，最多四人识别准确率超过92%，同时保持较低的误报率。
Multi-user identification could facilitate various large-scale identity-based services such as access control, automatic surveillance system, and personalized services, etc. Although existing solutions can identify multiple users using cameras, such vision-based approaches usually raise serious privacy concerns and require the presence of line-of-sight. Differently, in this paper, we propose MU-ID, a gait-based multi-user identification system leveraging a single commercial off-the-shelf (COTS) millimeter-wave (mmWave) radar. Particularly, MU-ID takes as input frequency-modulated continuous-wave (FMCW) signals from the radar sensor. Through analyzing the mmWave signals in the range-Doppler domain, MU-ID examines the users’ lower limb movements and captures their distinct gait patterns varying in terms of step length, duration, instantaneous lower limb velocity, and inter-lower limb distance, etc. Additionally, an effective spatial-temporal silhouette analysis is proposed to segment each user’s walking steps. Then, the system identifies steps using a Convolutional Neural Network (CNN) classifier and further identifies the users in the area of interest. We implement MU-ID with the TI AWR1642BOOST mmWave sensor and conduct extensive experiments involving 10 people. The results show that MU-ID achieves up to 97% single-person identification accuracy, and over 92% identification accuracy for up to four people, while maintaining a low false positive rate.