CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
Detecting Face with Densely Connected Face Proposal Network
Shifeng Zhang1,2; Xiangyu Zhu1,2; Zhen Lei1,2; Hailin Shi1,2; Xiaobo Wang1,2; Stan Z. Li1,2
2017
Conference NameCCF Chinese Conference Biometric Recognition
Conference Date2017-10
Conference Place中国深圳
Abstract

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. Specifically, we propose a novel face detector, dubbed the name Densely Connected Face Proposal Network (DCFPN), with high performance as well as real-time speed on the CPU devices. On the one hand, we subtly design a lightweight-butpowerful fully convolutional network with the consideration of efficiency and accuracy. On the other hand, we use the dense anchor strategy and propose a fair L1 loss function to handle small faces well. As a consequence, our 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 AFW, PASCAL face and FDDB datasets.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39049
Collection模式识别国家重点实验室_生物识别与安全技术
Corresponding AuthorZhen Lei
Affiliation1.Institute of Automation Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shifeng Zhang,Xiangyu Zhu,Zhen Lei,et al. Detecting Face with Densely Connected Face Proposal Network[C],2017.
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