  Eyewear devices, such as augmented reality displays, increasingly integrate
eye tracking but the first-person camera required to map a user's gaze to the
visual scene can pose a significant threat to user and bystander privacy. We
present PrivacEye, a method to detect privacy-sensitive everyday situations and
automatically enable and disable the eye tracker's first-person camera using a
mechanical shutter. To close the shutter in privacy-sensitive situations, the
method uses a deep representation of the first-person video combined with rich
features that encode users' eye movements. To open the shutter without visual
input, PrivacEye detects changes in users' eye movements alone to gauge changes
in the "privacy level" of the current situation. We evaluate our method on a
first-person video dataset recorded in daily life situations of 17
participants, annotated by themselves for privacy sensitivity, and show that
our method is effective in preserving privacy in this challenging setting.
