FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection
This addresses face detection for public security systems, but it appears incremental as it builds on existing encoder-decoder and context modeling approaches.
The paper tackles the problem of detecting small, blurry, occluded, and diverse-pose faces in outdoor surveillance images by proposing FHEDN, a feature hierarchy encoder-decoder network based on context modeling, which achieves promising performance on WIDER FACE and FDDB benchmarks.
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.