期刊
IEEE ACCESS
卷 7, 期 -, 页码 171609-171620出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2955757
关键词
Face detection; small face; convolutional neural network; deep learning
资金
- National Science and Technology Pillar Program of China [2012BAH48F02]
- National Natural Science Foundation of China [61801190]
- Nature Science Foundation of Jilin Province [20180101055JC]
- Outstanding Young Talent Foundation of Jilin Province [20180520029JH]
- China Postdoctoral Science Foundation [2017M611323]
- Industrial Technology Research and Development Funds of Jilin Province [2019C054-3]
- Fundamental Research Funds for the Central Universities, JLU
Tremendous strides have been made in face detection thanks to convolutional neural network. However, the performance of previous face detectors deteriorates dramatically as the face scale shrinks. In this paper, we propose a novel scale-invariant face detector, named Small Faces Attention (SFA) face detector, for better detecting small faces.We first present multi-branch face detection architecture which pays more attention to faces with small scale. Then, feature maps of neighbouring branches is fused so that the features coming from large scale can auxiliary detect hard faces with small scale. Finally, we simultaneously adopt multi-scale training and testing to make our model robust towards various scale. Comprehensive experiments show that SFA significantly improves face detection performance, especially on small faces. Our method achieves promising detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets, with competitive runtime speed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据