MagFace: Interference-Resistant Facial Gesture Recognition System on Cycling Glasses with Low-Power Magnetic Sensing

Facial interaction provides a safe, hands-free input method for cyclists. However, existing wearable facial gesture recognition suffers from severe interference in real-world conditions such as lighting, vibration, sweat, noise, and temperature changes. We present MagFace, an interference-resistant recognition system for cycling glasses using energy-efficient magnetic sensing. MagFace employs four pairs of magnetic silicone and magnetometers on the frame to capture subtle facial skin movements, operating at 30 Hz with a peak power of 150 mW. A tailored deep learning pipeline effectively learns magnetic signals for gesture classification. An evaluation (N=15) shows that MagFace required only one minute of training data to recognize six gestures across different cycling scenarios with high accuracy. A controlled conditions evaluation (N=8) shows MagFace’s robustness against strong lighting, wind, bumpy roads, and uphills. Finally, an in-the-wild evaluation (N=14) shows the stable performance of MagFace’s real-time system and demonstrates promising usability of MagFace.