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 CPU core for a
VGA-resolution image. Extra annotations and code have been made available at:
this https URL

本文介绍了一种名为 RetinaFace 的检测器，采用联合额外监督和自监督多任务学习，在各种尺度的人脸上实现了像素级别的人脸定位，并采用 5 个额外的面部标记，取得了显著的性能提升。