RetinaFace: Single-stage Dense Face Localisation in the Wild
Jiankang Deng * 1,2,4 Jia Guo * 2 Yuxiang Zhou 1 Jinke Yu 2 Irene Kotsia 3 Stefanos Zafeiriou1,4 1Imperial College London 2InsightFace 3Middlesex University London 4FaceSoft
Abstract
Though tremendous strides have been made in uncontrolled face detection, accurate and efficient face localisation in the wild remains an open challenge. This paper presents a robust single-stage face detector, named RetinaFace, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning. Specifically, We make contributions in the following five aspects: (1) We manually annotate five facial landmarks on the WIDER FACE dataset and observe significant improvement in hard face detection with the assistance of this extra supervision signal. (2) We further add a self-supervised mesh decoder branch for predicting a pixel-wise 3D shape face information in parallel with the existing supervised branches. (3) On the WIDER FACE hard test set, RetinaFace outperforms the state of the art average precision (AP) by $1.1\%$ (achieving AP equal to $91.4\%$ ). (4) On the IJB-C test set, RetinaFace enables state of the art methods (ArcFace) to improve their results in face verification (TAR= $89.59\%$ for FAR=1e-6). (5) By employing light-weight backbone networks, RetinaFace can run real-time on a single
中文速览
人脸检测在真实场景中面临尺度变化极大、遮挡严重等挑战，现有方法在精度和速度上难以兼顾。RetinaFace提出了一种单阶段人脸检测器，通过多任务学习同时预测人脸分类、边界框、五个关键点（facial landmarks）以及逐像素三维形状，其中关键点标注引入了额外的监督信号，三维网格解码（mesh decoder）则以自监督方式提供更丰富的形状信息。在公认最难的WIDER FACE硬测试集上，RetinaFace以91.4%的平均精度超越此前最优方法1.1个百分点，同时在IJB-C人脸验证任务中显著提升了ArcFace的识别准确率，使用轻量骨干网络时还能在单核CPU上对标准分辨率图像实现实时处理。这项工作表明，把更丰富的几何信息融入检测训练过程，能同时推动人脸检测与后续识别任务的性能上限。
原文 arXiv:1905.00641;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1905.00641v2