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  • Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network

    Author(s)
    Asif, M Daud Abdullah
    Gao, Yongsheng
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Gao, Yongsheng
    Year published
    2018
    Metadata
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    Abstract
    In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number ...
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    In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.
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    Conference Title
    2018 Digital Image Computing: Techniques and Applications (DICTA)
    DOI
    https://doi.org/10.1109/dicta.2018.8615831
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/384217
    Collection
    • Conference outputs

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