This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.

本文介绍了一种名为 Single Shot Scale-invariant Face Detector 的实时人脸检测器，它通过单个深度神经网络在各种尺度的人脸上表现出色，特别是对小尺寸的人脸有良好的检测效果。我们在提出一个尺度平等的人脸检测框架、通过尺度补偿锚匹配策略和最大化背景标签的方式来提高小尺寸人脸的召回率和减少假阳性率。通过 AFW、PASCAL face、FDDB 和 WIDER FACE 数据集的评测结果，该方法达到了当今最先进的检测性能，并可在 Nvidia Titan X (Pascal) 上为 VGA 分辨率图像提供 36 FPS 的响应速度。