Image Grid Recognition and Regression for Fast and Accurate Face Detection

CNN-based face detection methods have achieved significant progress in recent years. However, for high performance face detection, there are still many challenging problems, e.g., the speed-accuracy balance and the performance degradation in adverse conditions. In this paper, by taking advantage of the characteristic of CNN, we propose an effective anchor generation and bounding-box regression method that can make a good balance between speed and accuracy, and also work well in bad conditions. The classic structure of CNN produces pyramid-like feature maps due to the pooling or other downscale operations. According to the size of a feature map, we divide the image into grids. Each grid corresponds to a point in the feature map. We make the corresponding feature point responsible for identifying the content of the grid. If this grid area belongs to the face area, it is a natural anchor for face bounding-box regression. Since this anchor is square, it is reasonable to use it to predict the face bounding-box which is square-like. The points in the lower-level feature map correspond to smaller grids, which are dedicated to predicting the bounding-boxes of smaller faces. The points in the higher-level feature maps correspond to larger grids, which are responsible for predicting the bounding-boxes of larger faces. Hence our method can effectively detect multi-scale faces. With this effectiveness, our method can achieve a high detection accuracy using fewer parameters which leads to a fast detection speed. The experiments demonstrate the effectiveness of our method.