An Efficient Anchor-Free Face Detector with Attention Mechanisms

Enormous progress has been made in face detection tasks due to the rapid development of deep learning techniques. Meanwhile, debates arise on whether face detection should be treated as a generic object detection task or considered differently. In this paper, we design an efficient anchor-free face detector that focuses on a low flops regime and combines recent advances in generic object detection with the methods for detecting tiny faces. Specifically, we adopt the anchor-free Fully Convolutional One-Stage (FCOS) method with a recently developed Visual Attention Network (VAN) as the base detector. In accordance with the characteristics of the face dataset, we reallocate the computation across the network components by adjusting the network configurations of the base detector. Then we redesign the criteria for marking positive samples to realize a balanced distribution in pixel maps, and we also adopt the quadruple pixel prediction, which enables more positive samples matched with the model outputs. Under VGA resolution, our face detector achieves 70.5% in AP on the hard subset of the WIDER FACE dataset, while the computational cost is only 1.05 Gflops. This accuracy efficiency trade-off is comparable to state-of-the-art results.