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Face Detection With Different Scales Based on Faster R-CNN
Wu, Wenqi1,2; Yin, Yingjie1,2; Wang, Xingang1,2; Xu, De1,2
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2019-11-01
Volume49Issue:11Pages:4017-4028
Corresponding AuthorYin, Yingjie(yingjie.yin@ia.ac.cn)
AbstractIn recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, one of the remaining open challenges is the detection of small-scaled faces. The depth of the convolutional network can cause the projected feature map for small faces to be quickly shrunk, and most detection approaches with scale invariant can hardly handle less than 15 x 15 pixel faces. To solve this problem, we propose a different scales face detector (DSFD) based on Faster R-CNN. The new network can improve the precision of face detection while performing as real-time a Faster R-CNN. First, an efficient multitask region proposal network (RPN), combined with boosting face detection, is developed to obtain the human face ROI. Setting the ROI as a constraint, an anchor is inhomogeneously produced on the top feature map by the multitask RPN. A human face proposal is extracted through the anchor combined with facial landmarks. Then, a parallel-type Fast R-CNN network is proposed based on the proposal scale. According to the different percentages they cover on the images, the proposals are assigned to three corresponding Fast R-CNN networks. The three networks are separated through the proposal scales and differ from each other in the weight of feature map concatenation. A variety of strategies is introduced in our face detection network, including multitask learning, feature pyramid, and feature concatenation. Compared to state-of-the-art face detection methods such as UnitBox, HyperFace, FastCNN, the proposed DSFD method achieves promising performance on popular benchmarks including FDDB, AFW, PASCAL faces, and WIDER FACE.
KeywordDeep convolutional neural network (DCNN) deep learning face detection Faster R-CNN
DOI10.1109/TCYB.2018.2859482
WOS KeywordPOSE ESTIMATION ; LOCALIZATION ; RECOGNITION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61573349] ; National Natural Science Foundation of China[61703398] ; National High Technology Research and Development Program of China (863 Program)[2015AA042308]
Funding OrganizationNational Natural Science Foundation of China ; National High Technology Research and Development Program of China (863 Program)
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000476811000016
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21698
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorYin, Yingjie
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Wenqi,Yin, Yingjie,Wang, Xingang,et al. Face Detection With Different Scales Based on Faster R-CNN[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(11):4017-4028.
APA Wu, Wenqi,Yin, Yingjie,Wang, Xingang,&Xu, De.(2019).Face Detection With Different Scales Based on Faster R-CNN.IEEE TRANSACTIONS ON CYBERNETICS,49(11),4017-4028.
MLA Wu, Wenqi,et al."Face Detection With Different Scales Based on Faster R-CNN".IEEE TRANSACTIONS ON CYBERNETICS 49.11(2019):4017-4028.
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