Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/INFOCOM41043.2020.9155471 | IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS |
DocType | ISSN | Citations |
Conference | 0743-166X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Yang | 1 | 0 | 1.35 |
| Jian Liu | 2 | 289 | 59.26 |
| Yingying Chen | 3 | 2495 | 193.14 |
| Xiaonan Guo | 4 | 8 | 5.27 |
| Yucheng Xie | 5 | 2 | 2.73 |