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Component-Based Gender Identification Using Local Binary Patterns

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

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Abstract

In this paper a component-based gender identification model from facial images has been proposed. The paper enhances the gender identification by using individual facial components (forehead, eyes, nose, cheeks, mouth and chin). Group of frontal facial images are used to validate the proposed model, feature extraction technique Local Binary Patterns (LBP) is implemented, then KNN and SVM classification techniques are applied to accomplish the gender identification model. The results achieved in this research work show an improved accuracy rate when face components (eyes, nose, mouth) are used for gender identification instead of the whole facial image. These results indicate that there are some facial parts which are not necessary for facial image recognition related application like gender identification.

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Author information

Authors and Affiliations

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

    Salma M. Osman & Nahla Noor

  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. Nahla Noor
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  3. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Osman, S.M., Noor, N., Viriri, S. (2019). Component-Based Gender Identification Using Local Binary Patterns. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_25

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  • DOI: https://doi.org/10.1007/978-3-030-28377-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

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