  Recent advancements in augmented reality (AR) have enabled the use of various
sensors on smart glasses for applications like facial reconstruction, which is
vital to improve AR experiences for virtual social activities. However, the
size and power constraints of smart glasses demand a miniature and low-power
sensing solution. AUGlasses achieves unobtrusive low-power facial
reconstruction by placing inertial measurement units (IMU) against the temporal
area on the face to capture the skin deformations, which are caused by facial
muscle movements. These IMU signals, along with historical data on facial
action units (AUs), are processed by a transformer-based deep learning model to
estimate AU intensities in real-time, which are then used for facial
reconstruction. Our results show that AUGlasses accurately predicts the
strength (0-5 scale) of 14 key AUs with a cross-user mean absolute error (MAE)
of 0.187 (STD = 0.025) and achieves facial reconstruction with a cross-user MAE
of 1.93 mm (STD = 0.353). We also integrated various preprocessing and training
techniques to ensure robust performance for continuous sensing. Micro-benchmark
tests indicate that our system consistently performs accurate continuous facial
reconstruction with a fine-tuned cross-user model, achieving an AU MAE of 0.35.
