Face Recognition-Based Surveillance System in Mining Industry

  • Fadhil Hidayat Institut Teknologi Bandung
  • Ulva Elviani Institut Teknologi Bandung
  • Figo Agil Alunjati Institut Teknologi Bandung
  • Muhammad Furqan Alfuady Institut Teknologi Bandung
Keywords: face recognition, access control, mining construction, face recognition system

Abstract

Access control in mining construction areas is crucial for the operations of mining companies. This access control functions to secure and restrict unauthorized parties from mining activities. Violations of access rights in the mining industry result in significant losses for companies. This access control can also be utilized to record employee attendance, serving as input for the contract work system commonly applied in mining areas. Closed-circuit television (CCTV) is commonly used to monitor activities; however, the current use of CCTV still requires direct human observation, which may result in important events being overlooked. The functionality of these CCTVs can be enhanced to manage access rights and monitor employee attendance to support company operations through face recognition methods. In this study, a system design was carried out through a research approach to determine the technology to be used in the system. The development of a face recognition-based access control system was conducted based on system engineering methodology. This development includes system requirements analysis, the design of a face recognition-based access control system, implementation, and system performance evaluation. The resulting system was tested through simulation processes based on actual field conditions, and the test results showed that the system could recognize faces registered in the dataset and identify subjects not registered in the dataset with an accuracy of 60%, precision of 96%, recall of 58%, and an F-score of 72%. Additionally, the system was able to connect to a database to store face recognition results and then display them on an employee attendance monitoring dashboard. The delay between the face recognition system and actual time ranged from 2-4 seconds and was still tolerable.

Downloads

Download data is not yet available.

References

L. Li, X. Mu, S. Li and H. Peng, "A Review of Face Recognition Technology," in IEEE Access, vol. 8, pp. 139110-139120, 2020, doi: 10.1109/ACCESS.2020.3011028.

V. Kulkarni and K. Talele, "Video Analytics for Face Detection and Tracking," 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, 2020, pp. 962-965, doi: 10.1109/ICACCCN51052.2020.9362900.

D. Yadav, S. Maniar, K. Sukhani and K. Devadkar, "In-Browser Attendance System using Face Recognition and Serverless Edge Computing," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 01-06, doi: 10.1109/ICCCNT51525.2021.9580042.

Q. Zhai, "Integrated access control monitoring system based on face video detection," 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2022, pp. 580-585, doi: 10.1109/ICCECE54139.2022.9712709.

Wang, Mei, and Weihong Deng. "Deep Face Recognition: A Survey." arXiv preprint arXiv:1804.06655v9, last revised August 1, 2020. https://arxiv.org/abs/1804.06655.

F. Cahyono, W. Wirawan and R. Fuad Rachmadi, "Face Recognition System using Facenet Algorithm for Employee Presence," 2020 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, 2020, pp. 57-62, doi: 10.1109/ICOVET50258.2020.9229888.

Y. Taigman, M. Yang, M. Ranzato and L. Wolf, "Deep-face: Closing the gap to human-level performance in face verification", CVPR, 2014.

Y. Sun, X. Wang and X. Tang, "Deep learning face representation from predicting 10000 classes", CVPR, 2014b.

Florian Schroff, Dmitry Kalenichenko and James Philbin, "Facenet: A unified embedding for face recognition and clustering", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823, 2015.

B. Amos, B. Ludwiczuk and M. Satyanarayanan, "Openface: a general-purpose face recognition library with mobile applications", CMU-CS-16-118 CMU School of Computer Science, 2016.

J. Deng, J. Guo, J. Yang, N. Xue, I. Kotsia and S. Zafeiriou, "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 5962-5979, 1 Oct. 2022, doi: 10.1109/TPAMI.2021.3087709.

Published
2024-08-28
How to Cite
Hidayat, F., Elviani, U., Agil Alunjati, F., & Furqan Alfuady, M. (2024). Face Recognition-Based Surveillance System in Mining Industry . Jurnal Sistem Cerdas, 7(2), 226 - 236. https://doi.org/10.37396/jsc.v7i2.434
Section
Articles