High performance face detection remains a very challenging problem,
especially when there exists many tiny faces. This paper presents a novel
single-shot face detector, named Selective Refinement Network (SRN), which
introduces novel two-step classification and regression operations selectively
into an anchor-based face detector to reduce false positives and improve
location accuracy simultaneously. In particular, the SRN consists of two
modules: the Selective Two-step Classification (STC) module and the Selective
Two-step Regression (STR) module. The STC aims to filter out most simple
negative anchors from low level detection layers to reduce the search space for
the subsequent classifier, while the STR is designed to coarsely adjust the
locations and sizes of anchors from high level detection layers to provide
better initialization for the subsequent regressor. Moreover, we design a
Receptive Field Enhancement (RFE) block to provide more diverse receptive
field, which helps to better capture faces in some extreme poses. As a
consequence, the proposed SRN detector achieves state-of-the-art performance on
all the widely used face detection benchmarks, including AFW, PASCAL face,
FDDB, and WIDER FACE datasets. Codes will be released to facilitate further
studies on the face detection problem.

本文介绍了一种新的单次检测人脸算法 —— 选择性精化网络（SRN），通过引入新奇的两步分类和回归操作，可以减少假阳性，同时提高定位准确性，随即提出了两个模块，选择性两步分类（STC）和选择性两步回归（STR），并设计可接受的感受野扩展块（RFE）来更好地捕获面部的一些极端姿势，最终在 AFW，PASCAL face，FDDB 和 WIDER FACE 数据集上均取得了最新的性能。