  This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.
