  In this paper we address the problem of hallucinating high-resolution facial
images from unaligned low-resolution inputs at high magnification factors. We
approach the problem with convolutional neural networks (CNNs) and propose a
novel (deep) face hallucination model that incorporates identity priors into
the learning procedure. The model consists of two main parts: i) a cascaded
super-resolution network that upscales the low-resolution images, and ii) an
ensemble of face recognition models that act as identity priors for the
super-resolution network during training. Different from competing
super-resolution approaches that typically rely on a single model for upscaling
(even with large magnification factors), our network uses a cascade of multiple
SR models that progressively upscale the low-resolution images using steps of
$2\times$. This characteristic allows us to apply supervision signals (target
appearances) at different resolutions and incorporate identity constraints at
multiple-scales. Our model is able to upscale (very) low-resolution images
captured in unconstrained conditions and produce visually convincing results.
We rigorously evaluate the proposed model on a large datasets of facial images
and report superior performance compared to the state-of-the-art.
