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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 bytaking advantages of joint extra-supervised and self-supervised multi-tasklearning. Specifically, We make contributions in the following five aspects:(1) We manually annotate five facial landmarks on the WIDER FACE dataset andobserve significant improvement in hard face detection with the assistance ofthis extra supervision signal. (2) We further add a self-supervised meshdecoder branch for predicting a pixel-wise 3D shape face information inparallel with the existing supervised branches. (3) On the WIDER FACE hard testset, 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 enablesstate of the art methods (ArcFace) to improve their results in faceverification (TAR=89.59% for FAR=1e-6). (5) By employing light-weight backbonenetworks, RetinaFace can run real-time on a single CPU core for aVGA-resolution image. Extra annotations and code have been made available at:https://github.com/deepinsight/insightface/tree/master/RetinaFace.

RetinaFace: Single-stage Dense Face Localisation in the Wild