Face Attention Network: An Effective Face Detector for the Occluded Faces
This addresses the challenge of high false positives in occluded face detection for applications like surveillance and security, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of detecting occluded faces (e.g., with masks or sunglasses) by proposing the Face Attention Network (FAN), which achieves state-of-the-art results on benchmarks like WiderFace and MAFA without compromising speed.
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 detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.