Multi-scale Face Detection Based on Single Neural Network

Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% Mean Average Precision (MAP) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.