EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
This addresses the problem of biased and non-public datasets for researchers in computer vision, though it is incremental as it builds on existing face hallucination work.
The authors tackled the lack of public and ethnically diverse datasets for face hallucination by creating EDFace-Celeb-1M, a dataset with 1.7 million photos covering different countries and balanced race composition, and benchmarked state-of-the-art methods to reveal their performance and limitations.
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.