  In recent year, tremendous strides have been made in face detection thanks to
deep learning. However, most published face detectors deteriorate dramatically
as the faces become smaller. In this paper, we present the Small Faces
Attention (SFA) face detector to better detect faces with small scale. First,
we propose a new scale-invariant face detection architecture which pays more
attention to small faces, including 4-branch detection architecture and small
faces sensitive anchor design. Second, feature maps fusion strategy is applied
in SFA by partially combining high-level features into low-level features to
further improve the ability of finding hard faces. Third, we use multi-scale
training and testing strategy to enhance face detection performance in
practice. Comprehensive experiments show that SFA significantly improves face
detection performance, especially on small faces. Our real-time SFA face
detector can run at 5 FPS on a single GPU as well as maintain high performance.
Besides, our final SFA face detector achieves state-of-the-art detection
performance on challenging face detection benchmarks, including WIDER FACE and
FDDB datasets, with competitive runtime speed. Both our code and models will be
available to the research community.
