CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Detecting Face with Densely Connected Face Proposal Network
Zhang, Shifeng1,2,3; Zhu, Xiangyu1,2,3; Lei, Zhen1,2,3; Wang, Xiaobo1,2,3; Shi, Hailin1,2,3; Li, Stan Z.1,2,3
Source PublicationNEUROCOMPUTING
2018-04-05
Volume284Pages:119-127
SubtypeArticle
AbstractAccuracy and efficiency are two conflicting challenges for face detection, since effective models tend to be computationally prohibitive. To address these two conflicting challenges, our core idea is to shrink the input image and focus on detecting small faces. Reducing the image resolution can significantly improve the detection speed, but it also results in smaller faces that need to pay more attention. Specifically, we propose a novel face detector, dubbed the name Densely Connected Face Proposal Network (DCFPN), with high accuracy as well as CPU real-time speed. Firstly, we subtly design a lightweight-but-powerful fully convolution network with the consideration of efficiency and accuracy. Secondly, we present a dense anchor strategy and a scale-aware anchor matching scheme to improve the recall rate of small faces. Finally, a fair L1 loss is introduced to locate small faces well. As a consequence, our proposed method can detect faces at 30 FPS on a single 2.60 GHz CPU core and 250 FPS using a GPU for the VGA-resolution images. We achieve state-of-the-art performance on the common face detection benchmark datasets. (C) 2018 Elsevier B.V. All rights reserved.
KeywordFace Detection Small Face Region Proposal Network
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2018.01.012
WOS KeywordWILD
Indexed BySCI
Language英语
Funding OrganizationNational Key Research and Development Plan(2016YFC0801002) ; Chinese National Natural Science Foundation(61473291 ; AuthenMetric RD Funds ; 61572501 ; 61502491 ; 61572536)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000425883300013
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21950
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.Chinese Acad Sci, CBSR, Inst Automat, Beijing, Peoples R China
2.Chinese Acad Sci, NLPR, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Shifeng,Zhu, Xiangyu,Lei, Zhen,et al. Detecting Face with Densely Connected Face Proposal Network[J]. NEUROCOMPUTING,2018,284:119-127.
APA Zhang, Shifeng,Zhu, Xiangyu,Lei, Zhen,Wang, Xiaobo,Shi, Hailin,&Li, Stan Z..(2018).Detecting Face with Densely Connected Face Proposal Network.NEUROCOMPUTING,284,119-127.
MLA Zhang, Shifeng,et al."Detecting Face with Densely Connected Face Proposal Network".NEUROCOMPUTING 284(2018):119-127.
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