  In this paper, we present GazeTrak, the first acoustic-based eye tracking
system on glasses. Our system only needs one speaker and four microphones
attached to each side of the glasses. These acoustic sensors capture the
formations of the eyeballs and the surrounding areas by emitting encoded
inaudible sound towards eyeballs and receiving the reflected signals. These
reflected signals are further processed to calculate the echo profiles, which
are fed to a customized deep learning pipeline to continuously infer the gaze
position. In a user study with 20 participants, GazeTrak achieves an accuracy
of 3.6{\deg} within the same remounting session and 4.9{\deg} across different
sessions with a refreshing rate of 83.3 Hz and a power signature of 287.9 mW.
Furthermore, we report the performance of our gaze tracking system fully
implemented on an MCU with a low-power CNN accelerator (MAX78002). In this
configuration, the system runs at up to 83.3 Hz and has a total power signature
of 95.4 mW with a 30 Hz FPS.
