IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v6y2021i5p69-72.html
   My bibliography  Save this article

Improving the Performance of Facial Recognition System Using Artificial Neual Network

Author

Listed:
  • Asogwa Tochukwu Chijindu

    (Computer Science, Enugu State University of Science and Technology, Enugu, Nigeria)

  • Ugwu Edith Angela

    (Computer Science, Enugu State University of Science and Technology, Enugu, Nigeria)

  • Mbah David Luchi

    (Computer Science, Enugu State University of Science and Technology, Enugu, Nigeria)

Abstract

This work presents “improving the performance of facial recognition system using artificial neural network†. The aim is to develop a more reliable and précised face recognition system. This will be achieved using the AT&T database as the training dataset, image acquisition, image processing, and artificial neural network. The work will be implemented using image processing toolbox, image acquisition toolbox, statistics and machine learning toolbox and Mathlab. The accuracy was measured using the neural network performance evaluation toolbox and the result achieved is 97.6%.

Suggested Citation

  • Asogwa Tochukwu Chijindu & Ugwu Edith Angela & Mbah David Luchi, 2021. "Improving the Performance of Facial Recognition System Using Artificial Neual Network," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 6(5), pages 69-72, May.
    • Handle: RePEc:bjf:journl:v:6:y:2021:i:5:p:69-72
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-6-issue-5/69-72.pdf
    Download Restriction: no
    File URL: https://www.rsisinternational.org/virtual-library/papers/improving-the-performance-of-facial-recognition-system-using-artificial-neual-network/?utm_source=Netcore&utm_medium=Email&utm_content=sscollections25oct&utm_campaign=First&_gl=1*1l6f22y*_gcl_au*Nzg3MDc3MjYxLjE3MDIwMTAzMzE.*_ga*MTA1MTkzODcwMi4xNjk0MTkxNTI0*_ga_J3C1TKKSZ0*MTcwODU4MzE0MC4yNjIuMS4xNzA4NTgzMzk0LjYwLjAuMA..&_ga=2.115234311.1050379947.1708314550-1051938702.1694191524
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:6:y:2021:i:5:p:69-72. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://www.rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.