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A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network

IAES International Journal of Artificial Intelligence · Vol. 15 No. 4 (2026) · hlm. 3903–3918

DOI
10.11591/ijai.v15.i4.pp3903-3918
ISSN
2252-8938, 2089-4872
Terbit
2026-08-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.

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  • Sriwijaya University
  • Universitas Dinamika Bangsa
  • Udayana University
  • University of Technology Malaysia
  • Universitas Ahmad Dahlan
  • Mercu Buana University
  • Al Baha University
  • Organisasi lain
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Rahmat BudiartoAl Baha University14
Mohd. Yazid IdrisUniversity of Technology Malaysia9
Nurul AfifahSriwijaya University6
Kurniabudi KurniabudiUniversitas Dinamika Bangsa4
Sharipuddin SharipuddinUniversitas Dinamika Bangsa4
Benni PurnamaUniversitas Dinamika Bangsa4
Bhakti Yudho SupraptoSriwijaya University4
Tole SutiknoUniversitas Ahmad Dahlan3
Munawar Agus RiyadiDiponegoro University3
Darmawijoyo HanapiSriwijaya University3
Sutarno SutarnoSriwijaya University3
Abdurahman AbdurahmanSriwijaya University3
Lina HandayaniUniversitas Ahmad Dahlan2
Imam Much Ibnu SubrotoSultan Agung Islamic University2
Yaya Sudarya TrianaMercu Buana University2
Mohd Azam OsmanUniversiti Sains Malaysia2
M. Agus Syamsul ArifinUniversitas Bina Insan2
Eko Arip WinantoUniversitas Dinamika Bangsa2
Darmawijoyo DarmawijoyoSriwijaya University2
Bedine KerimUniversité de Nouakchott2
Yoppy SazakiSriwijaya University2
Muhammad SyahroyniSriwijaya University2
I Gede YusaUdayana University1
Dewi BungaUniversitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar1
Ni Ketut Supasti DharmawanUdayana University1
Desak Putu Dewi KasihUdayana University1
Adji PratomoMercu Buana University1
Muhammad Fermi PashaMonash University1
Zulhipni Reno Saputra ElsiUniversitas Muhammadiyah Palembang1
Bhakti Yudho SupraptoSriwijaya University1
Adi HermansyahSriwijaya University1
Ahmad HeryantoSriwijaya University1
Rici FirnandoSriwijaya University1
Hanna PertiwiSriwijaya University1
Siti Hajar OthmanUniversity of Technology Malaysia1
Ferdiansyah FerdiansyahPelita Harapan University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A novel embedded approach to face recognition using  multi-threaded controller based on weightless neural network</dc:title>
	<dc:creator>Zarkasi, Ahmad</dc:creator>
	<dc:creator>Satria, Hadipurnawan</dc:creator>
	<dc:creator>Primanita, Anggina</dc:creator>
	<dc:creator>Stiawan, Deris</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Center scan recognition; Embedded system; Face recognition; Multi-thread controller; Real-time processing; Weightless neural network</dc:subject>
	<dc:description xml:lang="en-US">This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-08-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/30728</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i4.pp3903-3918</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 3903-3918</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/30728/15356</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Ahmad Zarkasi, Hadipurnawan Satria, Anggina Primanita, Deris Stiawan</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>