级联的一阶段人脸检测算法

Face detection has achieved great success with the development of convolution neural network. However, it remains a challenging problem to detect small and blurred faces in unconstrained environment. This paper presents a novel cascade single-shot face detector, named Cascade Single Shot Face Detector (CSSD), which introduces novel cascade classification and regression network in an anchor-based face detector to reject false positives and improve location accuracy. We have contributed in the following three aspects: 1) proposing a feature enchanted and scale-invariable face detection architecture to process faces with different scales; 2) regressing bounding boxes of faces in two steps with a cascade method; 3) filtering negative anchors online after anchor refinement and rebalancing puzzle negative anchors and positive anchors with rate of 3:1. As a consequence, our method achieves state-of-the-art detection performance on FDDB and WIDER FACE dataset.