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

FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection