  Face detection has been well studied for many years and one of remaining
challenges is to detect small, blurred and partially occluded faces in
uncontrolled environment. This paper proposes a novel context-assisted single
shot face detector, named \emph{PyramidBox} to handle the hard face detection
problem. Observing the importance of the context, we improve the utilization of
contextual information in the following three aspects. First, we design a novel
context anchor to supervise high-level contextual feature learning by a
semi-supervised method, which we call it PyramidAnchors. Second, we propose the
Low-level Feature Pyramid Network to combine adequate high-level context
semantic feature and Low-level facial feature together, which also allows the
PyramidBox to predict faces of all scales in a single shot. Third, we introduce
a context-sensitive structure to increase the capacity of prediction network to
improve the final accuracy of output. In addition, we use the method of
Data-anchor-sampling to augment the training samples across different scales,
which increases the diversity of training data for smaller faces. By exploiting
the value of context, PyramidBox achieves superior performance among the
state-of-the-art over the two common face detection benchmarks, FDDB and WIDER
FACE. Our code is available in PaddlePaddle:
\href{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}{\url{https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection}}.
