Person Identification With Millimeter-Wave Radar in Realistic Smart Home Scenarios
Zhaoyang Xia, Genming Ding, Hui Wang, Feng Xu
Abstract
Compared with visual sensors that have light dependence and privacy intrusion issues, non-intrusion millimeter-wave (mmW) radars are more suitable for the daily person identification. In a realistic home scenario, there are new challenges that are not taken into account in the existing research. This letter attempts to address these issues such as multipath interference, complex walking process, and recognition robustness in smart home scenarios and designs a lightweight multi-branch convolutional neural network (CNN) with an Inception-Pool module and a Residual-Pool module to learn and classify gait Doppler features. The experimental results in a home living room scenario indicate that the designed mmW radar person identification system can achieve accurate and robust real-time identification performance.