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All Journal JURNAL PENDIDIKAN TAMBUSAI Restorica: Jurnal Ilmiah Ilmu Administrasi Negara dan Ilmu Komunikasi Dinasti International Journal of Education Management and Social Science Jurnal Bimas Islam Saraq Opat : Jurnal Administrasi Publik Al-MIKRAJ: Jurnal studi Islam dan Humaniora Jurnal Pengabdian Masyarakat Indonesia (JPMI) SWATANTRA AT-TAISIR: Journal of Indonesian Tafsir Studies Al Irfani: Journal of Al Qur'anic and Tafsir JURNAL ILMU DAN TEKNOLOGI PERIKANAN TANGKAP International Journal of Pertapsi Jurnal Ilmu Al-Qur'an dan Tafsir Jurnal Ilmiah Universitas Satya Negara Indonesia Jurnal Ekonomi dan Bisnis Islam Jurnal Riksa Cendikia Nusantara
Anwar, Muhammad Khoirul
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Comparison of Deep Learning Architectures for Facial Recognition Anwar, Muhammad Khoirul; Sugiantoro, Bambang
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 3 (2025): Dinasti International Journal of Education Management and Social Science (Febru
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i3.6080

Abstract

Facial recognition technology in modern security systems, including access control, identity verification, and digital devices. This study aims to compare the performance of three widely used deep learning architectures, Convolutional Neural Network (CNN), VGG16, and FaceNet512, in processing and identifying facial features. A quantitative approach was employed through computational experiments using facial image datasets. The performance of each model was evaluated using accuracy, precision, recall, and F1 score to assess its effectiveness in facial recognition tasks. The study revealed significant differences in the performance of each architecture, both in terms of recognition accuracy and processing efficiency. CNN, VGG16, and FaceNet512 each demonstrated distinct strengths and limitations. These findings provide valuable insights for selecting the most suitable deep learning architecture for practical and academic applications in facial biometric security systems.
Co-Authors Ariani, Devi Rahma Arief, Syaiful Bambang Sugiantoro Baratullah, Braham Maya Ediyanto Fitriyani, Nita Gultom, Armyn ha, Izzatusholek Hasan, Siti Jamilah Hidayat, Nadranabila Intani Hidayatullah Jannah, Zahrah Raudhatul Jauharoh, Arini Khasanah, Erika Nur Kurniati, Fauziah Marzuki, Angga Maulana, Manda Nurmaida, Anisa Oka Bayu Pratama Purnama, Nurlita Putri Asfarina Ifada Riena F Telussa Rofidah, Izzah Sari, Fiola Lolita Setiawati, Desti Sinera, Putri Mutiara Suciyono Zaldy Adrianto