Scale adaption-guided human face detection

Anchor-free based object detection has recently seen important progress benefiting from the advances in convolution neural networks. However, the detection performance for human faces is not so satisfactory. First of all, many existing anchor-free methods only focus on a certain scale of the feature map, such a mechanism often fails to perceive the important multi-scale context, resulting in a low recall rate of faces with large scale variations. To solve this problem, we propose to boost the face detection by adaptive learning to perceive the focal scale. To be specific, we design an online scale adaptation strategy to heuristically guide each layer detector to detect faces of different scales in multi-branch structures, which reduces outliers and improves recall rates. In additional, we also argue that the detection head with single convolution layer widely used in anchor-free methods is not robust enough to image context. Therefore, we augment the network by a context-aware detection module. The module dynamically generates different detectors for different input images based on their context to adapt to their image features, reducing the dependence on feature extraction ability of backbone network, and avoiding feature deviations in different scenes. Extensive experiments demonstrate that our method achieves significant performance gains compared to previous anchor-free methods and is comparable to the most advanced anchor-based face detection methods.