RGB-D face recognition using LBP with suitable feature dimension of depth image

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  • Additional Information
    • Publication Information:
      Institution of Engineering and Technology, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Computer engineering. Computer hardware
      LCC:Electronic computers. Computer science
    • Abstract:
      This study proposes a robust method for the face recognition from low-resolution red, green, and blue-depth (RGB-D) cameras acquired images which have a wide range of variations in head pose, illumination, facial expression, and occlusion in some cases. The local binary pattern (LBP) of the RGB-D images with the suitable feature dimension of Depth image is employed to extract the facial features. On the basis of error correcting output codes, they are fed to multiclass support vector machines (MSVMs) for the off-line training and validation, and then the online classification. The proposed method is called as the LBP-RGB-D-MSVM with the suitable feature dimension of the depth image. The effectiveness of the proposed method is evaluated by the four databases: Indraprastha Institute of Information Technology, Delhi (IIIT-D) RGB-D, visual analysis of people (VAP) RGB-D-T, EURECOM, and the authors. In addition, an extended database merged by the first three databases is employed to compare among the proposed method and some existing two-dimensional (2D) and 3D face recognition algorithms. The proposed method possesses satisfactory performance (as high as 99.10 ± 0.52% for Rank 5 recognition rate in their database) with low computation (62 ms for feature extraction) which is desirable for real-time applications.
    • File Description:
      electronic resource
    • ISSN:
      2398-3396
    • Relation:
      https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5045; https://doaj.org/toc/2398-3396
    • Accession Number:
      10.1049/iet-cps.2018.5045
    • Rights:
      Journal Licence: CC BY-NC-ND
    • Accession Number:
      edsdoj.86096f9d9d14d8dbead2ef9575906e1
  • Citations
    • ABNT:
      HAILAY BERIHU ABEBE; CHIH-LYANG HWANG. RGB-D face recognition using LBP with suitable feature dimension of depth image. IET Cyber-Physical Systems, [s. l.], 2019. Disponível em: . Acesso em: 21 nov. 2019.
    • AMA:
      Hailay Berihu Abebe, Chih-Lyang Hwang. RGB-D face recognition using LBP with suitable feature dimension of depth image. IET Cyber-Physical Systems. 2019. doi:10.1049/iet-cps.2018.5045.
    • APA:
      Hailay Berihu Abebe, & Chih-Lyang Hwang. (2019). RGB-D face recognition using LBP with suitable feature dimension of depth image. IET Cyber-Physical Systems. https://doi.org/10.1049/iet-cps.2018.5045
    • Chicago/Turabian: Author-Date:
      Hailay Berihu Abebe, and Chih-Lyang Hwang. 2019. “RGB-D Face Recognition Using LBP with Suitable Feature Dimension of Depth Image.” IET Cyber-Physical Systems. doi:10.1049/iet-cps.2018.5045.
    • Harvard:
      Hailay Berihu Abebe and Chih-Lyang Hwang (2019) ‘RGB-D face recognition using LBP with suitable feature dimension of depth image’, IET Cyber-Physical Systems. doi: 10.1049/iet-cps.2018.5045.
    • Harvard: Australian:
      Hailay Berihu Abebe & Chih-Lyang Hwang 2019, ‘RGB-D face recognition using LBP with suitable feature dimension of depth image’, IET Cyber-Physical Systems, viewed 21 November 2019, .
    • MLA:
      Hailay Berihu Abebe, and Chih-Lyang Hwang. “RGB-D Face Recognition Using LBP with Suitable Feature Dimension of Depth Image.” IET Cyber-Physical Systems, 2019. EBSCOhost, doi:10.1049/iet-cps.2018.5045.
    • Chicago/Turabian: Humanities:
      Hailay Berihu Abebe, and Chih-Lyang Hwang. “RGB-D Face Recognition Using LBP with Suitable Feature Dimension of Depth Image.” IET Cyber-Physical Systems, 2019. doi:10.1049/iet-cps.2018.5045.
    • Vancouver/ICMJE:
      Hailay Berihu Abebe, Chih-Lyang Hwang. RGB-D face recognition using LBP with suitable feature dimension of depth image. IET Cyber-Physical Systems [Internet]. 2019 [cited 2019 Nov 21]; Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.86096f9d9d14d8dbead2ef9575906e1&custid=s8280428