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Person identification based on facial biometrics in different lighting conditions

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 2 (2023)

DOI
10.11591/ijece.v13i2.pp2086-2092
ISSN
2722-2578, 2088-8708
Published
2023-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.

Collaboration network of the authors

4 authors · 4 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Kerbala
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ashwan Anwer AbdulmunemUniversity of Kerbala1
Mais Saad SafoqUniversity of Kerbala1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Person identification based on facial biometrics in different lighting conditions</dc:title><dc:creator>AbdulAbaus, Marem H.</dc:creator><dc:creator>Al-Shakarchy, Noor D.</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i2.pp2086-2092</dc:identifier><dc:identifier>10.11591/ijece.v13i2.pp2086-2092</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 2 (2023); 2086</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/28646/16412</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>