Poster Abstract: Person Identification under Heavy Occlusions Using Mmwave Radar

We propose mmWave-ocPID, a person identification (PID) method with millimeter-wave radar to identify individuals even when they are heavily occluded by obstacles. We collect a multi-modal dataset comprising mmWave radar point clouds and RGB images obtained from 9 human subjects, with over 180,000 frames for each modality. The mmWave-ocPID prototype employs a novel Neural Network integrated with two augmentation strategies for learning. Our initial experimental results show that mmWave-ocPID can achieve high identification accuracy, even when most of the human body of an individual is occluded in a controlled environment.