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

EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset