Fast and Accurate Face Detection using Feature Pyramid with Grid Anchors

CNN-based face detection methods have achieved significant progress in recent years. However, making a good balance between time cost and detection accuracy is still a challenging problem. Those methods which can reach a very high detection accuracy always have complicated networks and rely on expensive GPUs for inference, while those methods which have shallow networks and can run on common devices always lose detection accuracy to a large extent. In this paper, we propose an effective anchor generation and bounding-box regression method which can improve the detection accuracy by modifying the detection head of the popular detection networks. With this effectiveness, we can reduce the trainable weights of the network to speed up the inference while maintaining high accuracy. As a result, our method can get a better speed-accuracy balance. In our method, we divide the input image into grids according to the sizes of the pyramid-like feature maps produced by CNN. In training, those grids close to the center of the ground-truth bounding-boxes are selected as anchors. After training, the regression mapping from the anchors to the ground-truth bounding-boxes can be acquired by the exponential transformation we designed. Our method explicitly and strictly use feature maps in different levels to detect faces of different sizes. The higher-level feature maps and larger grid anchors are responsible for detecting larger faces, while the lower-level feature maps and smaller grid anchors are dedicated to detecting smaller faces. Therefore, our method is effective for detecting multi-scale faces. The experiments on both GPU and CPU demonstrate that our method is effective. Our source code is publicly available on https://github.com/zhouliguo/GAFace.