  An accurate computational model for image quality assessment (IQA) benefits
many vision applications, such as image filtering, image processing, and image
generation. Although the study of face images is an important subfield in
computer vision research, the lack of face IQA data and models limits the
precision of current IQA metrics on face image processing tasks such as face
superresolution, face enhancement, and face editing. To narrow this gap, in
this paper, we first introduce the largest annotated IQA database developed to
date, which contains 20,000 human faces -- an order of magnitude larger than
all existing rated datasets of faces -- of diverse individuals in highly varied
circumstances. Based on the database, we further propose a novel deep learning
model to accurately predict face image quality, which, for the first time,
explores the use of generative priors for IQA. By taking advantage of rich
statistics encoded in well pretrained off-the-shelf generative models, we
obtain generative prior information and use it as latent references to
facilitate blind IQA. The experimental results demonstrate both the value of
the proposed dataset for face IQA and the superior performance of the proposed
model.
