. Our FAN can successfully detect over 120 faces with occlusion. The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face * Equal contribution. † Work was done during an internship at Megvii Research. detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.
FAN REF proposes an anchor-level attention into RetinaNet to detect the occluded faces.
Face Attention Network: An Effective Face Detector for the Occluded Faces
ArXiv
ArXiv
Computer Science