Recent anchor-based deep face detectors have achieved promising performance,
but they are still struggling to detect hard faces, such as small, blurred and
partially occluded faces. A reason is that they treat all images and faces
equally, without putting more effort on hard ones; however, many training
images only contain easy faces, which are less helpful to achieve better
performance on hard images. In this paper, we propose that the robustness of a
face detector against hard faces can be improved by learning small faces on
hard images. Our intuitions are (1) hard images are the images which contain at
least one hard face, thus they facilitate training robust face detectors; (2)
most hard faces are small faces and other types of hard faces can be easily
converted to small faces by shrinking. We build an anchor-based deep face
detector, which only output a single feature map with small anchors, to
specifically learn small faces and train it by a novel hard image mining
strategy. Extensive experiments have been conducted on WIDER FACE, FDDB, Pascal
Faces, and AFW datasets to show the effectiveness of our method. Our method
achieves APs of 95.7, 94.9 and 89.7 on easy, medium and hard WIDER FACE val
dataset respectively, which surpass the previous state-of-the-arts, especially
on the hard subset. Code and model are available at
this https URL

本文提出了一种基于锚点的深度人脸检测器，通过在 hard images 上学习 small faces，并采用新型的 hard image mining 训练方法，在 WIDER FACE、FDDB、Pascal Faces 和 AFW 数据集上进行了实验，证明了该方法对 hard faces 检测的有效性。