  Face detection and alignment in unconstrained environment is always deployed
on edge devices which have limited memory storage and low computing power. This
paper proposes a one-stage method named CenterFace to simultaneously predict
facial box and landmark location with real-time speed and high accuracy. The
proposed method also belongs to the anchor free category. This is achieved by:
(a) learning face existing possibility by the semantic maps, (b) learning
bounding box, offsets and five landmarks for each position that potentially
contains a face. Specifically, the method can run in real-time on a single CPU
core and 200 FPS using NVIDIA 2080TI for VGA-resolution images, and can
simultaneously achieve superior accuracy (WIDER FACE Val/Test-Easy:
0.935/0.932, Medium: 0.924/0.921, Hard: 0.875/0.873 and FDDB discontinuous:
0.980, continuous: 0.732). A demo of CenterFace can be available at
https://github.com/Star-Clouds/CenterFace.
