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dc.contributor.authorBai, Yancheng
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2017-10-30T08:39:49Z
dc.date.available2017-10-30T08:39:49Z
dc.date.issued2017-08-24
dc.identifier.citationBai Y, Ghanem B (2017) Multi-scale Fully Convolutional Network for Face Detection in the Wild. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Available: http://dx.doi.org/10.1109/CVPRW.2017.259.
dc.identifier.doi10.1109/CVPRW.2017.259
dc.identifier.urihttp://hdl.handle.net/10754/625996
dc.description.abstractFace detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under grant 2016-KKI-2880.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8014993/
dc.titleMulti-scale Fully Convolutional Network for Face Detection in the Wild
dc.typeConference Paper
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.conference.date2017-07-21 to 2017-07-26
dc.conference.name30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
dc.conference.locationHonolulu, HI, USA
dc.contributor.institutionInstitute of Software, Chinese Academy of Science, Beijing, , , China
kaust.personBai, Yancheng
kaust.personGhanem, Bernard
kaust.grant.number2016-KKI-2880
dc.date.published-online2017-08-24
dc.date.published-print2017-07


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