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  • Fast Face Identification under Varying Pose from a Single 2-D Model View

    Author(s)
    Gao, Y
    Leung, MKH
    Wang, W
    Hui, SC
    Griffith University Author(s)
    Gao, Yongsheng
    Year published
    2001
    Metadata
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    Abstract
    One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. The authors present a simple and computationally efficient 3-D pose recovery methodology. It addresses the computationally expensive problem of current generic 3-D model pose recovery methods and thus is able to be used in real-time applications. Compared with the virtual view methods, the face identification system with the proposed pose recovery method demands much less storage space as it transforms the 2-D rotated face to the 2-D fronto-parallel view for subsequent identification rather than ...
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    One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. The authors present a simple and computationally efficient 3-D pose recovery methodology. It addresses the computationally expensive problem of current generic 3-D model pose recovery methods and thus is able to be used in real-time applications. Compared with the virtual view methods, the face identification system with the proposed pose recovery method demands much less storage space as it transforms the 2-D rotated face to the 2-D fronto-parallel view for subsequent identification rather than generating multiple virtual views for a single input face. Experiments evaluating the effectiveness of the technique are reported. The systems are compared with human performances and existing techniques
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    Journal Title
    IEE Proceedings-Vision, Image and Signal Processing
    Volume
    148
    Issue
    4
    Publisher URI
    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=955432
    DOI
    https://doi.org/10.1049/ip-vis:20010377
    Subject
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/60759
    Collection
    • Journal articles

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