Face Detection through Scale-Friendly Deep Convolutional Networks
This addresses face detection for practical applications by eliminating the need for image pyramids, though it is incremental in improving scale handling.
The paper tackles the problem of detecting faces across a wide range of scales by designing a convolutional network-based detector that integrates specialized networks into a unified model, achieving 76.4 average precision on WIDER FACE and 96% recall on FDDB with 7 fps.
In this paper, we share our experience in designing a convolutional network-based face detector that could handle faces of an extremely wide range of scales. We show that faces with different scales can be modeled through a specialized set of deep convolutional networks with different structures. These detectors can be seamlessly integrated into a single unified network that can be trained end-to-end. In contrast to existing deep models that are designed for wide scale range, our network does not require an image pyramid input and the model is of modest complexity. Our network, dubbed ScaleFace, achieves promising performance on WIDER FACE and FDDB datasets with practical runtime speed. Specifically, our method achieves 76.4 average precision on the challenging WIDER FACE dataset and 96% recall rate on the FDDB dataset with 7 frames per second (fps) for 900 * 1300 input image.