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In Your Face: Person Identification Through Ratios of Distances Between Facial Features

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Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12034))

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Abstract

These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. The purpose of this paper is to introduce yet another innovative approach for face recognition. The human face consists of multiple features that when considered together produces a unique signature that identifies a single person. Building upon this premise, we are studying the identification of faces by producing ratios from the distances between the different features on the face and their locations in an explainable algorithm with the possibility of future inclusion of multiple spectrum and 3D images for data processing and analysis.

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Acknowledgements

This study was supported by the Scientific Research from Technical University of Technology Sydney, School of Electrical and Data Engineering and DIVE IN AI.

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Authors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, Australia

    Mohammad Alsawwaf & Zenon Chaczko

  2. DIVE IN AI, Wroclaw, Poland

    Zenon Chaczko & Marek Kulbacki

  3. Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    Mohammad Alsawwaf

  4. Polish-Japanese Academy of Information Technology, R&D Center, Warsaw, Poland

    Marek Kulbacki

Authors
  1. Mohammad Alsawwaf
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  2. Zenon Chaczko
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  3. Marek Kulbacki
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Corresponding author

Correspondence to Mohammad Alsawwaf .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Alsawwaf, M., Chaczko, Z., Kulbacki, M. (2020). In Your Face: Person Identification Through Ratios of Distances Between Facial Features. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_44

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  • DOI: https://doi.org/10.1007/978-3-030-42058-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42057-4

  • Online ISBN: 978-3-030-42058-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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