  Recent deep face hallucination methods show stunning performance in
super-resolving severely degraded facial images, even surpassing human ability.
However, these algorithms are mainly evaluated on non-public synthetic
datasets. It is thus unclear how these algorithms perform on public face
hallucination datasets. Meanwhile, most of the existing datasets do not well
consider the distribution of races, which makes face hallucination methods
trained on these datasets biased toward some specific races. To address the
above two problems, in this paper, we build a public Ethnically Diverse Face
dataset, EDFace-Celeb-1M, and design a benchmark task for face hallucination.
Our dataset includes 1.7 million photos that cover different countries, with
balanced race composition. To the best of our knowledge, it is the largest and
publicly available face hallucination dataset in the wild. Associated with this
dataset, this paper also contributes various evaluation protocols and provides
comprehensive analysis to benchmark the existing state-of-the-art methods. The
benchmark evaluations demonstrate the performance and limitations of
state-of-the-art algorithms.
