Face Detection Using Hierarchical Fully Convolutional Networks

Face detection in unconstrained environment is a challenge problem. Recent studies show that deep convolutional networks (DCNs) have achieved outstanding performance on this task, but most of them have multiple stages (e.g., region proposal, classification), which are complex and time-consuming in practice. In this paper, we propose a fully convolutional network (FCN) framework which can be trained straightforward in an end-to-end manner. In our network, hierarchical feature layers with different resolutions are used to detect different scale faces. For each hierarchical layer, a specific default boxes set with different aspect ratios and scales is associated with each map cell. At prediction time, the network generates confidence scores for the default boxes and produces offsets of default boxes to get better bounding boxes of faces. The predictions of each hierarchical layer are combined into final detection result. Experimental results on the AFW and FDDB datasets confirm the effectiveness of our method.