  Because of affected by weather conditions, camera pose and range, etc.
Objects are usually small, blur, occluded and diverse pose in the images
gathered from outdoor surveillance cameras or access control system. It is
challenging and important to detect faces precisely for face recognition system
in the field of public security. In this paper, we design a based on context
modeling structure named Feature Hierarchy Encoder-Decoder Network for face
detection(FHEDN), which can detect small, blur and occluded face with hierarchy
by hierarchy from the end to the beginning likes encoder-decoder in a single
network. The proposed network is consist of multiple context modeling and
prediction modules, which are in order to detect small, blur, occluded and
diverse pose faces. In addition, we analyse the influence of distribution of
training set, scale of default box and receipt field size to detection
performance in implement stage. Demonstrated by experiments, Our network
achieves promising performance on WIDER FACE and FDDB benchmarks.
