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Current face detectors utilize anchors to frame a multi-task learning problemwhich combines classification and bounding box regression. Effective anchordesign and anchor matching strategy enable face detectors to localize facesunder large pose and scale variations. However, we observe that more than 80%correctly predicted bounding boxes are regressed from the unmatched anchors(the IoUs between anchors and target faces are lower than a threshold) in theinference phase. It indicates that these unmatched anchors perform excellentregression ability, but the existing methods neglect to learn from them. Inthis paper, we propose an Online High-quality Anchor Mining Strategy (HAMBox),which explicitly helps outer faces compensate with high-quality anchors. Ourproposed HAMBox method could be a general strategy for anchor-basedsingle-stage face detection. Experiments on various datasets, including WIDERFACE, FDDB, AFW and PASCAL Face, demonstrate the superiority of the proposedmethod. Furthermore, our team win the championship on the Face Detection testtrack of WIDER Face and Pedestrian Challenge 2019. We will release the codeswith PaddlePaddle.

HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces