Score-specific Non-maximum Suppression and Coexistence Prior for Multi-scale Face Detection

Face detection is an ultimate component to support various visual facial related tasks. However, detecting faces with extremely low resolution or high occlusion is still an open problem. In this paper, we propose a two-step general approach to refine the performance of modern face detectors according to human's high-level context-aware ability. First, we propose Score-specific Non-Maximum Suppression (SNMS) to preserve overlapped faces. Second, we consider the coexistence prior among faces in the scene, which could raise the sensitivity of face detection in the crowd. When integrating our approach to the existing face detectors, most of them have better results on a challenging benchmark (WIDER FACE) and a newly proposed dataset (Faces in Crowd, FIC) made by us. Codes are available on https://github.com/AIoTP/SNMSandCoexistence.