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

Abstract

Because of affected by weather conditions, camera pose and range, etc. objects are usually small, blurry, occluded and diverse pose in the images, which are 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 context modeling network named Feature Hierarchy Encoder-Decoder Network for face detection (FHEDN), which can detect small, blurry and occluded faces hierarchy by hierarchy from the end to the beginning in a single stage. The proposed network consists of encoder and decoder subnetworks. The encoder subnetwork constructs a multi-scale feature hierarchy pyramid through VGG-16 as backbone network. The decoder subnetwork models context semantic information around face and fuses it into the feature hierarchy for face detection. In addition, we analyze the influence of distribution of training set, scale of feature hierarchy and receipt field size on the detection performance in implement stage. The experiments demonstrate that our network achieves the promising performance on AFW, PASCAL FACE, WIDER FACE and FDDB benchmarks.