  This paper studies face recognition (FR) and normalization in surveillance
imagery. Surveillance FR is a challenging problem that has great values in law
enforcement. Despite recent progress in conventional FR, less effort has been
devoted to surveillance FR. To bridge this gap, we propose a Feature Adaptation
Network (FAN) to jointly perform surveillance FR and normalization. Our face
normalization mainly acts on the aspect of image resolution, closely related to
face super-resolution. However, previous face super-resolution methods require
paired training data with pixel-to-pixel correspondence, which is typically
unavailable between real-world low-resolution and high-resolution faces. FAN
can leverage both paired and unpaired data as we disentangle the features into
identity and non-identity components and adapt the distribution of the identity
features, which breaks the limit of current face super-resolution methods. We
further propose a random scale augmentation scheme to learn resolution robust
identity features, with advantages over previous fixed scale augmentation.
Extensive experiments on LFW, WIDER FACE, QUML-SurvFace and SCface datasets
have shown the effectiveness of our method on surveillance FR and
normalization.

Computer Vision and Pattern Recognition;Machine Learning