Face detection has achieved significant progress in recent years. However,
high performance face detection still remains a very challenging problem,
especially when there exists many tiny faces. In this paper, we present a
single-shot refinement face detector namely RefineFace to achieve high
performance. Specifically, it consists of five modules: Selective Two-step
Regression (STR), Selective Two-step Classification (STC), Scale-aware Margin
Loss (SML), Feature Supervision Module (FSM) and Receptive Field Enhancement
(RFE). To enhance the regression ability for high location accuracy, STR
coarsely adjusts locations and sizes of anchors from high level detection
layers to provide better initialization for subsequent regressor. To improve
the classification ability for high recall efficiency, STC first filters out
most simple negatives from low level detection layers to reduce search space
for subsequent classifier, then SML is applied to better distinguish faces from
background at various scales and FSM is introduced to let the backbone learn
more discriminative features for classification. Besides, RFE is presented to
provide more diverse receptive field to better capture faces in some extreme
poses. Extensive experiments conducted on WIDER FACE, AFW, PASCAL Face, FDDB,
MAFA demonstrate that our method achieves state-of-the-art results and runs at
$37.3$ FPS with ResNet-18 for VGA-resolution images.

该论文提出了一种名为 RefineFace 的单次精炼人脸检测器，它由五个模块组成：选择性二步回归、选择性二步分类、比例感知边缘损失、特征监督模块和感受野增强。实验结果表明，该方法在 WIDER FACE、AFW、PASCAL Face、FDDB、MAFA 等数据集上均取得了最先进的结果，并在 VGA 分辨率图像中以 37.3 FPS 的速度运行。