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Automatic Identification of Ear Patterns Based on Convolutional Neural Network

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New Trends in Information and Communications Technology Applications (NTICT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2096))

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Abstract

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in an image, possesses the unique characteristic that no two individuals share the same ear patterns. Consequently, our research proposes a system for individual identification based on ear traits, comprising three main stages: (1) pre-processing to extract the ear pattern (region of interest) from input images, (2) feature extraction, and (3) classification. Convolutional Neural Network (CNN) is employed for the feature extraction and classification tasks. The system remains invariant to scaling, brightness, and rotation. Experimental results demonstrate that the proposed system achieved an accuracy of 99.86% for all datasets.

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Author information

Authors and Affiliations

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

    Saba A. Tuama

  2. Department of Computer Science, College of Science, Al-Mustansiriyah University, Baghdad, Iraq

    Jamila H. Saud

  3. Department of Geology, College of Science, University of Baghdad, Baghdad, Iraq

    Omar Fitian Rashid

Authors
  1. Saba A. Tuama
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  2. Jamila H. Saud
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  3. Omar Fitian Rashid
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Corresponding author

Correspondence to Saba A. Tuama .

Editor information

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

    Mouayad A. Sahib

  3. University of Babylon, Babylon, Iraq

    Safaa O. Al-Mamory

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

    Jaafar A. Aldhaibani

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

    Ali N. Al-Shuwaili

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

    Haitham S. Hasan

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

    Rula A. Hamid

  8. University of Babylon, Babylon, Iraq

    Ali K. Idrees

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Tuama, S.A., Saud, J.H., Rashid, O.F. (2024). Automatic Identification of Ear Patterns Based on Convolutional Neural Network. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2023. Communications in Computer and Information Science, vol 2096. Springer, Cham. https://doi.org/10.1007/978-3-031-62814-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-62814-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62813-9

  • Online ISBN: 978-3-031-62814-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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