SAFD: single shot anchor free face detector

The anchor-free based face detection methods can cover a large range of scales and perform better in the speed. However, their performance still bears a large gap compared with anchor-based methods, especially for detecting small faces. Because they are troubled by the context modeling and scale imbalance problems. In this study, to address these problems, we propose a novel single shot anchor-free face detector (SAFD) for detecting multi-scale faces by leveraging the multi-scale context aware information of multi-layer features. In the SAFD, we use the dilated convolution layers and attention mechanism to select the informative features that can accommodate to different scales. We also propose a scale-aware sampling strategy to mitigate the scale imbalance problem by adaptivity selecting the positive training samples. The experimental results on two public benchmark datasets, Wider Face and FDDB dataset, demonstrate that our SAFD can achieve competitive performance with the anchor-based detectors while with lower computation cost.