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    Small Face Detection with Deep Learning Approaches

    Tuli SH 2021.pdf (23.75Mb)
    Access Status
    Open access
    Authors
    Tuli, Sabrina Hoque
    Date
    2021
    Supervisor
    Wan-Quan Liu
    Ling Li
    Senjian An
    Type
    Thesis
    Award
    MPhil
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/86208
    Collection
    • Curtin Theses
    Abstract

    This thesis considers small face detection in uncontrolled environments and develops robust deep learning approaches for this challenging problem. A novel multi-scale face detector is developed by integrating novel anchor design, efficient regression loss and additional detection layers. Several multi-scale dense convolutional networks are developed to boost up the detection of small faces. Experimental results on public face databases demonstrate that the proposed methods outperform the state-of-the-art methods (e.g. YOLOv3) for detection of small faces.

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