  Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for
recreational photography. However, aerial photographs of public places often
contain faces of bystanders thus leading to a perceived or actual violation of
privacy. To address this issue, we propose to pseudo-randomly modify the
appearance of face regions in the images using a privacy filter that prevents a
human or a face recogniser from inferring the identities of people. The filter,
which is applied only when the resolution is high enough for a face to be
recognisable, adaptively distorts the face appearance as a function of its
resolution. Moreover, the proposed filter locally changes its parameters to
discourage attacks that use parameter estimation. The filter exploits both
global adaptiveness to reduce distortion and local hopping of the parameters to
make their estimation difficult for an attacker. In order to evaluate the
efficiency of the proposed approach, we use a state-of-the-art face recognition
algorithm and synthetically generated face data with 3D geometric image
transformations that mimic faces captured from an MAV at different heights and
pitch angles. Experimental results show that the proposed filter protects
privacy while reducing distortion and exhibits resilience against attacks.
