  The widespread deployment of surveillance cameras for facial recognition
gives rise to many privacy concerns. This study proposes a privacy-friendly
alternative to large scale facial recognition. While there are multiple
techniques to preserve privacy, our work is based on the minimization principle
which implies minimizing the amount of collected personal data. Instead of
running facial recognition software on all video data, we propose to
automatically extract a high quality snapshot of each detected person without
revealing his or her identity. This snapshot is then encrypted and access is
only granted after legal authorization. We introduce a novel unsupervised face
image quality assessment method which is used to select the high quality
snapshots. For this, we train a variational autoencoder on high quality face
images from a publicly available dataset and use the reconstruction probability
as a metric to estimate the quality of each face crop. We experimentally
confirm that the reconstruction probability can be used as biometric quality
predictor. Unlike most previous studies, we do not rely on a manually defined
face quality metric as everything is learned from data. Our face quality
assessment method outperforms supervised, unsupervised and general image
quality assessment methods on the task of improving face verification
performance by rejecting low quality images. The effectiveness of the whole
system is validated qualitatively on still images and videos.
