IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v4y2020i1id19129.html

Recognize Facial Impression using Artificial Neural Network

Author

Listed:
  • Montaser Hassan Bashir Ali
  • Osman Mudathir Elfadil

Abstract

This paper aims to design an artificial neural network to discover the impression by recognizing the expression of the human face. To achieve this goal, the artificial neural network was analyzed and to create patterns of the database containing a set of images with different expressions. The learning process of the network was also conducted through patterns training. The extent to which patterns of online training were recognized was compared to the true values of expressions. The grid was trained in 200 patterns and the anomalies were removed. Then re-learned the network again and analyzed the network performance by comparing the real expression with the expected expression and outputting the error for the network appearing. Impression recognition in the grid applied a three-layer back propagation model, with an average error of 0.321. The performance of the artificial neural network in the recognition of impressions was 80%

Suggested Citation

  • Montaser Hassan Bashir Ali & Osman Mudathir Elfadil, 2020. "Recognize Facial Impression using Artificial Neural Network," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(1), January.
  • Handle: RePEc:epw:ejece0:v:4:y:2020:i:1:id:19129
    DOI: 10.24018/ejece.2020.4.1.129
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19129
    File Function: Abstract page
    Download Restriction: no

    File URL: https://eu-opensci.org/index.php/ejece/article/download/19129/11086
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.24018/ejece.2020.4.1.129?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:epw:ejece0:v:4:y:2020:i:1:id:19129. 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: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    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.