Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | NEUROCOMPUTING |
2018-04-05 | |
卷号 | 284页码:119-127 |
文章类型 | Article |
摘要 | Accuracy 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. |
关键词 | Face Detection Small Face Region Proposal Network |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.neucom.2018.01.012 |
关键词[WOS] | WILD |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Key Research and Development Plan(2016YFC0801002) ; Chinese National Natural Science Foundation(61473291 ; AuthenMetric RD Funds ; 61572501 ; 61502491 ; 61572536) |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000425883300013 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21950 |
专题 | 多模态人工智能系统全国重点实验室_生物识别与安全技术 |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Neurocomputing_DCFPN(4200KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
Neurocomputing_DCFPN(4200KB) | 开放获取 | -- | 浏览 下载 |
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