Saliency guided low-light face detection

被引：0

作者：

Li K. ^{[1
]}

Zhong H. ^{[2
,3
,4
]}

Gao X. ^{[2
]}

Weng C. ^{[2
]}

Chen Z. ^{[2
,3
,4
]}

Li Y. ^{[2
]}

Wang S. ^{[2
,3
,4
]}

机构：

[1] School of Microelectronics, University of Chinese Academy of Sciences, Beijing

[2] Institute of Microelectronics of Chinese Academy of Sciences, Beijing

[3] Chinese Academy of Sciences R & D Center for Internet of Things, Wuxi

[4] Chinese Academy of Sciences R & D Center for Internet of Things, Wuxi

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Computer vision; Deep neural network; Face detection; Low light; Saliency guided;

D O I：

10.13700/j.bh.1001-5965.2020.0469

中图分类号：

学科分类号：

摘要：

To deal with the problem that it is hard for convolution neural network to do face detection in low light environment, we propose a method combining image saliency and deep learning and apply it to low-light face detection, which integrates saliency and the original RGB channels of the image into neural network training. Sufficient experiments are implemented on DARK FACE, a low-light face dataset, and the results show that the proposed low-light face detection method achieves better detection accuracy than the existing mainstream face detection algorithms on DARK FACE, thus confirming the validity of the proposed method. © 2021, Editorial Board of JBUAA. All right reserved.

引用

页码：572 / 584

页数：12

共 37 条

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