• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Learning discriminative subregions and pattern orders for facial gender classification

    Thumbnail
    View/Open
    Embargoed until: 2021-09-01
    File version
    Accepted Manuscript (AM)
    Author(s)
    Chen, Zhong
    Edwards, Andrea
    Gao, Yongsheng
    Zhang, Kun
    Griffith University Author(s)
    Gao, Yongsheng
    Year published
    2019
    Metadata
    Show full item record
    Abstract
    Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based ...
    View more >
    Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based feature vector selection scheme, is then developed to highlight the gender characteristics. As a result, the discriminative subregions and pattern orders are constructed as the feature representation for facial gender classification. We evaluate our proposed method on four benchmark datasets (i.e., FRGC 2.0, FERET, LFW and UND) for gender classification and demonstrate its interpretability, effectiveness and efficiency compared with state-of-the-art methods.
    View less >
    Journal Title
    Image and Vision Computing
    Volume
    89
    DOI
    https://doi.org/10.1016/j.imavis.2019.06.012
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/386695
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander