Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism

doi:10.1007/s11633-022-1327-2

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	Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism
	Lin Song 1; Jin-Fu Yang 1,2; Qing-Zhen Shang 1; Ming-Ai Li 1
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2022
卷号	19 期号:3 页码:247-256
摘要	Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.
其他摘要	Face detection; global context; attention mechanism; computer vision; deep learning
DOI	10.1007/s11633-022-1327-2
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55944
专题	学术期刊_Machine Intelligence Research
作者单位	1.Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2.Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China
推荐引用方式 GB/T 7714	Lin Song,Jin-Fu Yang,Qing-Zhen Shang,et al. Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism[J]. Machine Intelligence Research,2022,19(3):247-256.
APA	Lin Song,Jin-Fu Yang,Qing-Zhen Shang,&Ming-Ai Li.(2022).Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism.Machine Intelligence Research,19(3),247-256.
MLA	Lin Song,et al."Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism".Machine Intelligence Research 19.3(2022):247-256.

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