Human Face Recognition and Attendance Recording with Surveillance Videos Using Convolutional Neural Networks (CNNs)

Authors

  • Sara Mohammed Al Rashdi Department of Information Systems, College of EMIS, University of Nizwa, Nizwa, Oman
  • Ebtesam Al Shereiqi Department of Information Systems, College of EMIS, University of Nizwa, Nizwa, Oman
  • Dr. Sallam O. F. Khairy Department of Information Systems, College of EMIS, University of Nizwa, Nizwa, Oman

Keywords:

Automated Attendance, Convolutional Neural Networks, Facial Recognition, Surveillance Video, Deep Learning

Abstract

Objectives: Nowadays, the advancement of technology has made attendance automation an indispensable factor that schools and organizations need to implement in educational and organizational settings. Methods: This research paper utilizes and examines how Convolutional Neural Networks (CNNs) can build an accurate automated attendance management system using facial recognition technology. Findings: The proposed system utilizes surveillance videos, which offer highly reliable real-time attendance recording while eliminating the tedious mistakes that manual and traditional methods would produce. Novelty: The performance evaluation demonstrates that the CNN-based system surpasses HOG and KCF algorithms, achieving 99% accuracy, 95% precision, and 94% recall rates, thereby providing educational institutions with an effective solution to their modern surveillance needs.

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Published

2024-06-15

How to Cite

l Rashdi, S. . ., l Shereiqi, E. ., & O. F. Khairy, S. . (2024). Human Face Recognition and Attendance Recording with Surveillance Videos Using Convolutional Neural Networks (CNNs). PLOMS AI, 4(1). Retrieved from https://plomscience.com/journals/index.php/PLOMSAI/article/view/26

Issue

Vol. 4 No. 1 (2024): 2024 Issue

Section

Computer

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