Face detection from low-light images is challenging due to limited photos and
inevitable noise, which, to make the task even harder, are often spatially
unevenly distributed. A natural solution is to borrow the idea from
multi-exposure, which captures multiple shots to obtain well-exposed images
under challenging conditions. High-quality implementation/approximation of
multi-exposure from a single image is however nontrivial. Fortunately, as shown
in this paper, neither is such high-quality necessary since our task is face
detection rather than image enhancement. Specifically, we propose a novel
Recurrent Exposure Generation (REG) module and couple it seamlessly with a
Multi-Exposure Detection (MED) module, and thus significantly improve face
detection performance by effectively inhibiting non-uniform illumination and
noise issues. REG produces progressively and efficiently intermediate images
corresponding to various exposure settings, and such pseudo-exposures are then
fused by MED to detect faces across different lighting conditions. The proposed
method, named REGDet, is the first `detection-with-enhancement' framework for
low-light face detection. It not only encourages rich interaction and feature
fusion across different illumination levels, but also enables effective
end-to-end learning of the REG component to be better tailored for face
detection. Moreover, as clearly shown in our experiments, REG can be flexibly
coupled with different face detectors without extra low/normal-light image
pairs for training. We tested REGDet on the DARK FACE low-light face benchmark
with thorough ablation study, where REGDet outperforms previous
state-of-the-arts by a significant margin, with only negligible extra
parameters.

本文提出了一种名为 REGDet 的新颖的 “检测与增强” 框架，通过有效地抑制不均匀的照明和噪声问题，显著提高了低光弱面部检测性能，并且没有额外的低 / 正常光图像对进行培训。