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    Multi-scale Fully Convolutional Network for Face Detection in the Wild

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    Type
    Conference Paper
    Authors
    Bai, Yancheng
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    KAUST Grant Number
    2016-KKI-2880
    Date
    2017-08-24
    Online Publication Date
    2017-08-24
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/625996
    
    Metadata
    Show full item record
    Abstract
    Face 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.
    Citation
    Bai 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.
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under grant 2016-KKI-2880.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
    Conference/Event name
    30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
    DOI
    10.1109/CVPRW.2017.259
    Additional Links
    http://ieeexplore.ieee.org/document/8014993/
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPRW.2017.259
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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