This repository contains the implementation and code resources for our paper: MMIDNet: Secure Human Identification Using Millimeter-wave Radar and Deep Learning (link: https://doi.org/10.1109/MECO62516.2024.10577920). 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.
- MmWave radar point-cloud input: avoid voxelization to save GPU memory usage.
- Hybrid architecture: PointNet + CNN + LSTM + MLP
- Privacy-preserving: Operates without cameras or wearables, ensuring non-intrusive monitoring.
This project is licensed under the MIT License.
You are free to use, modify, and distribute this project, provided that you include the original copyright and license notice in any copy of the project or substantial portions of it.
See the LICENSE file for more details.