  State-of-the-art deep neural network models have reached near perfect face
recognition accuracy rates on controlled high-resolution face images. However,
their performance is drastically degraded when they are tested with very
low-resolution face images. This is particularly critical in surveillance
systems, where a low-resolution probe image is to be matched with
high-resolution gallery images. super-resolution techniques aim at producing
high-resolution face images from low-resolution counterparts. While they are
capable of reconstructing images that are visually appealing, the
identity-related information is not preserved. Here, we propose an
identity-preserving end-to-end image-to-image translation deep neural network
which is capable of super-resolving very low-resolution faces to their
high-resolution counterparts while preserving identity-related information. We
achieved this by training a very deep convolutional encoder-decoder network
with a symmetric contracting path between corresponding layers. This network
was trained with a combination of a reconstruction and an identity-preserving
loss, on multi-scale low-resolution conditions. Extensive quantitative
evaluations of our proposed model demonstrated that it outperforms competing
super-resolution and low-resolution face recognition methods on natural and
artificial low-resolution face data sets and even unseen identities.
