Facial Recognition Using an mmWave Radar

Facial recognition is an important user authentication function in many systems. Traditional vision-based approaches are vulnerable to spoofing attacks and are affected by lighting conditions. In this work, we propose a human facial recognition system using a single millimeter-wave (mmWave) radio. Our system actively transmits the mmWave signal toward the user's face and receives the echoes that contain the geometric face information. We design a novel signal processing pipeline to defend against spoofing attacks and extract facial features. The extracted features are fed into a CNN-based classifier to recognize user identities. We implement and evaluate our system on a tiny and low-cost mmWave radar (25$). The experimental results show that the system can detect the spoofing samples with an average accuracy of 99.12% and achieves an average recognition accuracy of 97.5% over 21 volunteers in different environments.