Paper Info
Title
Face Attention Network: An Effective Face Detector for the Occluded Faces.
Abstract
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.
Year
Venue
Field
2017
arXiv: Computer Vision and Pattern Recognition
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Face detection,Recall,Detector,False positive paradox
DocType
Volume
Citations 
Journal
abs/1711.07246
17
PageRank 
References 
Authors
0.57
22
3
Name
Order
Citations
PageRank
Wang Jianfeng121333.78
Ye Yuan2259.72
Gang Yu338219.85