In this paper, we propose a novel face detection network with three novel
contributions that address three key aspects of face detection, including
better feature learning, progressive loss design and anchor assign based data
augmentation, respectively. First, we propose a Feature Enhance Module (FEM)
for enhancing the original feature maps to extend the single shot detector to
dual shot detector. Second, we adopt Progressive Anchor Loss (PAL) computed by
two different sets of anchors to effectively facilitate the features. Third, we
use an Improved Anchor Matching (IAM) by integrating novel anchor assign
strategy into data augmentation to provide better initialization for the
regressor. Since these techniques are all related to the two-stream design, we
name the proposed network as Dual Shot Face Detector (DSFD). Extensive
experiments on popular benchmarks, WIDER FACE and FDDB, demonstrate the
superiority of DSFD over the state-of-the-art face detectors.

本文提出了一种名为 “Dual Shot Face Detector (DSFD)” 的新型人脸检测网络，通过三个关键方面的改进：改善特征学习、优化损失设计、数据增强，达到了超越现有人脸检测器的效果。