Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods

Authors

  • Suherman Achmad Universitas Budi Luhur
  • Nazori AZ Universitas Budi Luhur
  • Achmad Solichin Universitas Budi Luhur
(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i2.213

Keywords:

Eigenface, Haar Cascade Classifier, presence system

Abstract

Using biometric technology for recording attendance in the school environment is still not widely done by researchers. In this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. The solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). The built system can record the presence of students when entering the classroom and when returning home or out of class. Proposed methods for identifying student attendance are the Haar Cascade and Eigenface algorithms. The system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. Based on the test results, the method can provide accurate and fast facial recognition results. The presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity.

Downloads

Download data is not yet available.

References

Abuzar, M., Ahmad, A. Bin, & Ahmad, A. A. Bin. (2020). A Survey on Student Attendance System Using Face Recognition. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 1252–1257. https://doi.org/10.1109/ICRITO48877.2020.9197815

Ahmed Khan, D., Rizvi Assistant Professor, A., & Scholar, D. (2021). AI based Facial Recognition Technology and Criminal Justice: Issues and Challenges. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(14), 3384–3392. https://www.turcomat.org/index.php/turkbilmat/article/view/10923

Alburaiki, M. S. M., Johar, G. M., Helmi, R. A. A., & Alkawaz, M. H. (2021). Mobile Based Attendance System: Face Recognition and Location Detection using Machine Learning. 2021 IEEE 12th Control and System Graduate Research Colloquium, ICSGRC 2021 - Proceedings, 177–182. https://doi.org/10.1109/ICSGRC53186.2021.9515221

Amri, A., & Rahmata, T. (2016). Pengenalan Wajah Menggunakan Metode Fisherface untuk Mendukung Sistem Akademik. Proceeding Seminar Nasional Ilmu Komputer (Seminasik), 1(1), 39–43. http://jurnal.umuslim.ac.id/index.php/seminasik/article/view/474

Anarki, G. A., Auliasari, K., & Orisa, M. (2021). Penerapan Metode Haar Cascade Pada Aplikasi Deteksi Masker. JATI (Jurnal Mahasiswa Teknik Informatika), 5(1), 179–186. https://doi.org/10.36040/JATI.V5I1.3214

Arisandi, D., Syahputra, M. F., Putri, I. L., Purnamawati, S., Rahmat, R. F., & Sari, P. P. (2018). A real time mobile-based face recognition with fisherface methods. Journal of Physics: Conference Series, 978(1), 012038. https://doi.org/10.1088/1742-6596/978/1/012038

Behera, G. S. (2020, December 24). Face Detection with Haar Cascade. Towards Data Science. https://towardsdatascience.com/face-detection-with-haar-cascade-727f68dafd08

Firasari, E., Cahyanti, F. L. D., Sarasati, F., & Widiastuti, W. (2022). Comparison Of Eigenface and Fisherface Methods For Face Recognition. Techno Nusa Mandiri, 19(2), 125–130. https://doi.org/10.33480/TECHNO.V19I2.3470

Rijal, Y., & Ariefianto, R. D. (2008). Deteksi Wajah Berbasis Segmentasi Model Warna Menggunakan Template Matching Pada Objek Bergerak. Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 1907–5022. https://journal.uii.ac.id/Snati/article/view/891

Rodavia, M. R. D., Bernaldez, O., & Ballita, M. (2017). Web and mobile based facial recognition security system using Eigenfaces algorithm. Proceedings of 2016 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2016, 86–92. https://doi.org/10.1109/TALE.2016.7851776

Samet, R., & Tanriverdi, M. (2017). Face recognition-based mobile automatic classroom attendance management system. Proceedings - 2017 International Conference on Cyberworlds, CW 2017 - in Cooperation with: Eurographics Association International Federation for Information Processing ACM SIGGRAPH, 2017-January, 253–256. https://doi.org/10.1109/CW.2017.34

Satwikayana, S., Wibowo, S. A., & Vendyansyah, N. (2021). Sistem Presensi Mahasiswa Otomatis Pada Zoom Meeting Menggunakan Face Recognition Dengan Metode Convulitional Neural Network Berbasis Web. JATI (Jurnal Mahasiswa Teknik Informatika), 5(2), 785–793. https://doi.org/10.36040/JATI.V5I2.3762

Septyanto, M. W., Sofyan, H., Jayadianti, H., Simanjuntak, O. S., & Dessyanto, B. P. (2020). Aplikasi Presensi Pengenalan Wajah Dengan Menggunakan Algoritma Haar Cascade Classifier. Telematika : Jurnal Informatika Dan Teknologi Informasi, 16(2), 87–96. https://doi.org/10.31315/TELEMATIKA.V16I2.3182

Simaremare, H., & Kurniawan, A. (2016). Perbandingan Akurasi Pengenalan Wajah Menggunakan Metode LBPH dan Eigenface dalam Mengenali Tiga Wajah Sekaligus secara Real-Time. SITEKIN: Jurnal Sains, Teknologi Dan Industri, 14(1), 66–71. https://doi.org/10.24014/SITEKIN.V14I1.2703

Sulistiyo, W., Suyanto, B., Hestiningsih, I., Mardiyono, M., & Sukamto, S. (2014). Rancang Bangun Prototipe Aplikasi Pengenalan Wajah untuk Sistem Absensi Alternatif dengan Metode Haar Like Feature dan Eigenface. JTET (Jurnal Teknik Elektro Terapan), 3(2), 93–98. https://doi.org/10.32497/JTET.V3I2.180

Downloads

Published

2023-03-25

How to Cite

Achmad, S., AZ, N., & Solichin, A. (2023). Mobile Based Student Presence System Using Haar Cascade and Eigenface Facial Recognition Methods. Jurnal Riset Informatika, 5(2), 219–228. https://doi.org/10.34288/jri.v5i2.213

Issue

Section

Articles