Open-Set Occluded Person Identification With mmWave Radar

Radio frequency sensors can penetrate non-metal objects and provide complementary information to vision sensors for person identification (PID) purposes. However, there is a lack of research on millimeter wave (mmWave) radar for PID under occlusions, particularly in addressing the open-set recognition problem. Thus, we propose an open-set occluded PID (OSO-PID) framework that can deal with various obstacle and occlusion scenarios with open-set recognition capability. We first introduce a new dataset, mmWave-ocPID, comprising mmWave radar measurements and RGB-depth images, collected from 23 human subjects. We next design a novel neural network, mm-PIDNet, for occluded person identification using mmWave radar measurements. mm-PIDNet incorporates a transformer encoder, a bidirectional long short-term memory module, and a novel supervised contrastive learning module to improve PID performance. For open-set recognition, we enhance the mmWave radar-based PID method by integrating supervised contrastive learning with the Weibull models, which can identify out-of-distribution samples. We perform extensive indoor experiments with a variety of obstacles and occlusion scenarios. Our experimental results show that mm-PIDNet achieves an F1-score of 0.93 on average, outperforming state-of-the-art methods by up to 13.41% for occluded cases. For open-set PID, the OSO-PID framework achieves an F1-score above 0.8 when the openness is less than 14.36%.