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A Comprehensive Review in Using the Advances of Deep Learning in the 3D Race Classification

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New Trends in Information and Communications Technology Applications (NTICT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1764))

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Abstract

Human faces can reveal not just the human identity, but even demographic characteristics such as ethnicity and gender. Recently, the researchers get the advantages of Deep Learning techniques in developing face recognition systems implemented on both 2D and 3D face datasets. However, the usefulness of Deep learning in analyzing facial features of 3D faces gender, and ethnicity are examined in literature with only three main perspectives: data representation, augmentation, and comparison using the several commonly used format of 3D face representation such as depth images, point clouds, normal maps, triangular mesh, and horizontal disparity images. Many algorithms are implemented by authors on popular 3D datasets including FRGC v2, 3D-Texas, and BU3D-FE. In this work, we highlight the advantages of using the deep learning 3D representation in “race recognition” approaches and refer the researchers to the important related works in this field by comparing them according to their distinguishing metrics and invariant conditions support and the used techniques and datasets.

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  1. Babylon University, Babylon, Iraq

    Noor H. Reda

  2. School of Engineering, Kerbala University, Kerbala, Iraq

    Hawraa Abbas

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  1. Noor H. Reda
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  2. Hawraa Abbas
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Correspondence to Noor H. Reda .

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Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Safaa O. Al-Mamory

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  4. University of Information Technology and Communications, Baghdad, Iraq

    Loay E. George

  5. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. Open University of Tanzania, Dar es Salaam, Tanzania

    George S. Oreku

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Reda, N.H., Abbas, H. (2023). A Comprehensive Review in Using the Advances of Deep Learning in the 3D Race Classification. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2022. Communications in Computer and Information Science, vol 1764. Springer, Cham. https://doi.org/10.1007/978-3-031-35442-7_5

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  • DOI: https://doi.org/10.1007/978-3-031-35442-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35441-0

  • Online ISBN: 978-3-031-35442-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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