Hierarchical bilinear network for high performance face detection

Deep Convolutional Networks (DCNs) have achieved great success in face detection. Most architectures of the DCN-based methods, however, suffer from multiple separated steps and large-size models, which increase the training complexity and also slow down the testing speed. In this paper, we propose an efficient end-to-end architecture, called Hierarchical Bilinear Network (HBN), for fast and accurate face detection. It mainly consists of two parts: the Backbone Network and the Bilinear Network. The Backbone Network generates hierarchical feature maps for efficiently characterizing faces of different scales, while the Bilinear Network classifies the regions and regresses the face bounding-boxes on each feature map by introducing the Inception module and weights sharing. Benefited from the characters of the proposed architecture, it obtains a better comprehensive performance regarding the model effectiveness, running efficiency, and parameter size, compared with other DCN-based methods. Extensive experimental results demonstrate that our detector achieves competitive accuracy on both the FDDB database and the WIDER FACE database, while still runs in real time (about 69 FPS on a Titan Black GPU) with a tiny size (2.2 MB) model.