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All Journal Tekno - Pedagogi : Jurnal Teknologi Pendidikan Jurnal Ilmiah Peuradeun Proceedings of the 1st UR International Conference on Educational Sciences E-Mabis: Jurnal Ekonomi Manajemen dan Bisnis Tafhim Al-'Ilmi : Jurnal Pendidikan dan Pemikiran Islam SINAU : Jurnal Ilmiah Pendidikan dan Pembelajaran Buletin Pengabdian AT-TABAYYUN: Journal Islamic Studies Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert Jurnal Epistemic : Jurnal Ilmiah Pendidikan al Kimiya : Jurnal Ilmu Kimia dan Terapan Al-Irfan: Journal Of Arabic Literature and Islamic Studies
Muhammad Nazar
Prodi Pendidikan Kimia, FKIP Unsyiah, Darussalam, Banda Aceh 23111
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Journal of Advanced Computer Knowledge and Algorithms

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Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory Tawakal, Rayendra; Nazar, Muhammad; Asri, Rahmadi
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.18889

Abstract

A real-time facial detection system for identifying young and old faces has been developed using a combination of Template Matching and Fuzzy Associative Memory (FAM) methods. This study aims to improve accuracy in detecting facial age, particularly from images captured via a webcam. The system was tested across four categories: Old Men, Young Men, Old Women, and Young Women, with 10 image samples per category. The results indicate that the system achieved an accuracy rate of 83%. The Young Men category exhibited the best performance with 100% accuracy, while detection errors occurred in the Old Men and Old Women categories, with a false positive rate of 30%. The system proved to be more effective at detecting young faces than old faces. The primary challenge of this study was managing the complex variation in the patterns of older faces. Thus, further research is required to enhance the system’s performance in detecting older faces and reduce the false positive rate.
Co-Authors . Rusman, . Abdul Gani Aman Aman Asri, Rahmadi Astina Ria, Astina Basri Basri Fadlia, Faradilla Febrianisa, Sindy Handayani, Winarni Heri, Kusai Heriyana Herliana, Fitria Ichwana Ichwana Iris, Mardjoko Izzaty, Nur Julia Dewi Latifah Hanum Lutfi, Fauzi Maman Paturahman Muliana, Rahmi Nasrullah Nasrullah Naufal Bachri, Naufal Puspita, Kana Putra, Andhika Jaya Restiana, Dinda Lisma Rita Sari Septian, Amar Siregar, Widyana Verawaty Sulastri Sulastri Syafira, Defy Tawakal, Rayendra Yayan Sudrajat Yulianti, Nuri Zahra Amalia