EDFace-Celeb-1 M: Benchmarking Face Hallucination With a Million-Scale Dataset

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-1 M, and design a benchmark task for face hallucination. Our dataset includes 1.7 million photos that cover different countries, with relatively balanced race composition. To the best of our knowledge, it is the largest-scale 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. https://github.com/HDCVLab/EDFace-Celeb-1M.

1 Introduction

Human faces contain important identity information and are central to various vision applications, such as face alignment [1], [2], [3], face parsing [4], [5] and face identification [6], [7]. However, most of these applications require high-quality images as input and the approaches perform less favorably in low-resolution conditions. To alleviate the issue, the task of face hallucination, or face super-resolution, aims to super-resolve low-resolution face images to their high-resolution counterparts, thus facilitating effective face analysis.