  Face super-resolution, also known as face hallucination, which is aimed at
enhancing the resolution of low-resolution (LR) one or a sequence of face
images to generate the corresponding high-resolution (HR) face images, is a
domain-specific image super-resolution problem. Recently, face super-resolution
has received considerable attention, and witnessed dazzling advances with deep
learning techniques. To date, few summaries of the studies on the deep
learning-based face super-resolution are available. In this survey, we present
a comprehensive review of deep learning techniques in face super-resolution in
a systematic manner. First, we summarize the problem formulation of face
super-resolution. Second, we compare the differences between generic image
super-resolution and face super-resolution. Third, datasets and performance
metrics commonly used in facial hallucination are presented. Fourth, we roughly
categorize existing methods according to the utilization of face-specific
information. In each category, we start with a general description of design
principles, present an overview of representative approaches, and compare the
similarities and differences among various methods. Finally, we envision
prospects for further technical advancement in this field.
