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Dynamic Local Ternary Patterns for Gender Identification Using Facial Components

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Computer Vision and Graphics (ICCVG 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12334))

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Abstract

Automated human face detection is a topic of significant interest in the field of image processing and computer vision. In the last years much emphasis has been on using facial images to extract gender of human which has become much used in many modern programs used in mobile phones. Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Ternary Pattern (LTP) are popular appearance-based methods for extracting the feature from images. This paper proposes an improved Local Ternary Pattern technique called Dynamic Local Ternary Patterns to address the problem of gender classification using frontal facial images. The proposed Dynamic Local Ternary Patterns solve the limitation found in LBP (poorly performed in illumination variation and random noise) and LDP (produces inconsistence pattern), by increasing the number of the face components, applying the four cardinal directions namely North, East, West, South and using a dynamic threshold in LTP instead of a static one.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, Sudan University of Science and Technology, Khartoum, Sudan

    Salma M. Osman

  2. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

Authors
  1. Salma M. Osman
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  2. Serestina Viriri
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Correspondence to Serestina Viriri .

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Editors and Affiliations

  1. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Leszek J. Chmielewski

  2. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Ryszard Kozera

  3. Institute of Information Technology, Warsaw University of Life Sciences - SGGW, Warsaw, Poland

    Arkadiusz Orłowski

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Osman, S.M., Viriri, S. (2020). Dynamic Local Ternary Patterns for Gender Identification Using Facial Components. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2020. Lecture Notes in Computer Science(), vol 12334. Springer, Cham. https://doi.org/10.1007/978-3-030-59006-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-59006-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59005-5

  • Online ISBN: 978-3-030-59006-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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