MMIDNet: Secure Human Identification Using Millimeter-wave Radar and Deep Learning

This paper introduces an innovative approach using deep learning for human identification utilizing millimeter-wave (mmWave) radar technology. Unlike conventional vision methods, our approach ensures privacy and accuracy in various indoor settings. Leveraging partial PointNet, Convolutional Neural Network (CNN), and Bi-directional Long Short-Term Memory (Bi-LSTM) network components, we propose a unique neural network architecture named MMIDNet, designed to directly process point cloud data from mmWave radar. Our system achieves an impressive identification accuracy of 92.4% for 12 individuals. The research encompasses data collection, system design, and evaluation, highlighting the potential of mmWave radar combined with deep learning for secure and efficient human identification in Internet of Things (IoT) applications.