Security Service Monitoring Using Face Recognition, Near Field Communication and Geolocation Technology
DOI:
https://doi.org/10.34010/injiiscom.v6i1.13976Keywords:
Field control, Face recognition, Geolocation, NFC, MonitoringAbstract
In a company that provides security services, monitoring, and field control activities are carried out daily to ensure that all designated checkpoints are properly supervised. This research aimed at facilitating the management in summarizing the field control activity reports and enhancing the supervision of the field security personnel conducting field control. The application is developed using Golang, JavaScript, and Kotlin programming languages and utilizes PostgreSQL as its database. The application is web-based for administrative personnel and mobile-based for field security personnel. The technology used in building this application includes face recognition, GPS, and location-based service and NFC reader. Based on the implementation and testing results, it is found that the developed application functions according to the established workflow. The fastest face recognition detection time was 1.22 seconds, and the RFID tag was successfully detected at a distance of less than 4 cm and an average time of 0.378 seconds, and the use of geolocation provides accurate position results.
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Chang, Y.-H., Hu, C.-L., Hwang, Y.-L., Ou, C.-W., & Hsu, F.-H. (2018). Fake GPS Defender: A Server-side Solution to Detect Fake GPS. Proceedings of the 3rd International Conference on Advances in Computation, Communications and Services, pp. 38–41.
Chen, P., Geng, X., Zou, M., Xu, Q., & Tan, D. (2020). Development and Optimization of Check-in System Based on Face Recognition Technology. IOP Conference Series: Materials Science and Engineering, 782(5), pp. 1–9.
Fortuna, I., & Khaeruzzaman, Y. (2022). Implementation of OCR and Face Recognition on Mobile Based Voting System Application in Indonesia. IJNMT (International Journal of New Media Technology), 9(1), pp. 20–27.
Hasan, S. (2023). Utilitas Etika Profesi Konsultan IT Terhadap Optimisme Kepercayaan Perusahaan. Jurnal Teknologi Terapan and Sains 4.0, 4(1), pp. 917–922.
Hayati, L. N. (2019). Sistem Monitoring Karyawan Dengan Metode Lbs (Location Based Service) Berbasis Android. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(1), pp. 61–66.
Irsan, M., & Sulaiman, H. (2019). Pemanfaatan Teknologi Near Field Communication(Nfc) Dan Face Recognition Sebagai Media Monitoring Keamanan (Patroli) Anggota Security. Faktor Exacta, 12(3), pp. 167–179.
Nithya, J., Vignesh, S. R., Devadarsan, A., & Venkatesh, S. (2022). AI based contactless attendance monitoring and management system. International Journal of Health Sciences, 6(3), pp. 10600–10614.
Orabi, M., Al Aghbari, Z., & Kamel, I. (2023). FogLBS: Utilizing fog computing for providing mobile Location-Based Services to mobile customers. Pervasive and Mobile Computing, 94, pp. 101832.
Qi, S., Zuo, X., Feng, W., & Naveen, I. G. (2022). Face Recognition Model Based on MTCNN and Facenet. 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022. https://doi.org/10.1109/ICMNWC56175.2022.10031806
Rusia, M. K., & Singh, D. K. (2023). A comprehensive survey on techniques to handle face identity threats: challenges and opportunities. Multimedia Tools and Applications, 82(2), pp. 1669–1748.
Sahin, V. H., Oztel, I., & Yolcu Oztel, G. (2022). Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application. Journal of Medical Systems, 46(11), pp. 46–79.
Setiawan, E. B., & Setiyadi, A. (2021). Mapping application for Greater Bandung Area using Web Technology. Journal of Engineering Research (Kuwait), 9, pp. 1–15.
Sharma, H., Sewani, H., Garg, R., & Kashef, R. (2021). Face Mask Detection: A Real-Time Android Application Based on Deep Learning Modeling. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021, pp. 106–112.
Shivanna, P., & Venkatesiah, S. S. (2024). Biometric Identification for a Secured Environment Using AI-Based Facial Recognition. International Journal of Safety and Security Engineering, 14(1), pp. 185–190.
Susanto, F., Fauziah, F., & Andrianingsih, A. (2021). Lecturer Attendance System using Face Recognition Application an Android-Based. Journal of Computer Networks, Architecture and High Performance Computing, 3(2), pp. 167–173.
Tanış, M., & Erhan Yalçın, M. (2024). A statistical method of GPS spoofing detection using power spectral density for hardware. International Journal of Circuit Theory and Applications, 52(5), pp. 2560–2573.
Tenhunen, S., Männistö, T., Luukkainen, M., & Ihantola, P. (2023). A systematic literature review of capstone courses in software engineering. In Information and Software Technology Vol. 159, p. 107191.
Wang, B., Li, H., Ren, X., & Guo, Y. (2023). An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services. Sensors, 23(11), pp. 1–18.
Widjaja, A. E., Harjono, N. J., Hery, Mitra, A. R., & Haryani, C. A. (2023). Automated Class Attendance Management System using Face Recognition: an Application of Viola-Jones Method. Journal of Applied Data Sciences, 4(4), pp. 431–440.
William, I., Ignatius Moses Setiadi, D. R., Rachmawanto, E. H., Santoso, H. A., & Sari, C. A. (2019). Face Recognition using FaceNet (Survey, Performance Test, and Comparison). Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019.
Xiao, J., Jiang, G., & Liu, H. (2018). A Lightweight Face Recognition Model based on MobileFaceNet for Limited Computation Environment. EAI Endorsed Transactions on Internet of Things, 7(27), pp.1–9.
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Published
2024-12-08
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Articles
How to Cite
[1]
“Security Service Monitoring Using Face Recognition, Near Field Communication and Geolocation Technology”, Int. J. Inform. Inf. Sys. and Comp. Eng., vol. 6, no. 1, pp. 70–81, Dec. 2024, doi: 10.34010/injiiscom.v6i1.13976.
