Implementation of Face Recognition-Based Personal Information Management System using Hikvision AI Cameras for UNUSA Community
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Nanda Dwi Cahyo Wibowo
Rizqi Putri Nourma Budiarti
Abstract
Personal Information Management (PIM) at the University of Nahdlatul Ulama Surabaya (UNUSA) continues to face challenges, particularly in managing attendance within the eSorogan system and the UNUSA Academic Information System (SIM). This study proposes and implements a face recognition-based PIM system designed to automate the identification and recording of members of the UNUSA academic community. The system integrates AI-powered cameras with a web-based dashboard, referred to as the Lobby PIM Dashboard, which displays real-time information related to recognized individuals. The workflow begins with image capture and face detection by the AI camera, followed by data transmission to the server via an API for identification and information retrieval. The system generates detection logs and attendance records, which are stored and presented through the dashboard interface. The results demonstrate that the proposed system can support more efficient attendance management and provide accessible academic information for lecturers and students. Overall, this implementation contributes to improving administrative processes and enhancing the effectiveness of academic services at UNUSA.
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Copyright (c) 2025 Nanda Dwi Cahyo Wibowo, Rizqi Putri Nourma Budiarti
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References
W. Jones, J. D. Dinneen, R. Capra, A. Diekema, and M. Pérez-Quiñones, “Personal information management (PIM),” Encyclopedia of library and information science, pp. 3584–3605, 2017, doi: 10.1081/e-elis4-120053695.
I. Fourie, “Librarians alert: How can we exploit what is happening with personal information management (PIM), reference management and related issues?,” Library Hi Tech, vol. 29, no. 3, pp. 550–556, 2011, doi: 10.1108/07378831111174477.
W. P. Jones and J. Teevan, Personal information management, vol. 14. University of Washington Press Seattle, WA, 2007. doi: 10.1515/9780295800684.
A. Lush, “Fundamental personal information management activities–organisation, finding and keeping: A literature review,” Aust Libr J, vol. 63, no. 1, pp. 45–51, 2014, doi: 10.1080/00049670.2013.875452.
D. Elsweiler and I. Ruthven, “Towards task-based personal information management evaluations,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 23–30. doi: 10.1145/1277741.1277748.
E. A. Yani and A. M. Syam, “Implementasi Personal Information Management (PIM) Mahasiswa Tingkat Akhir Pada Program Studi Ilmu Perpustakaan,” Reslaj: Religion Education Social Laa Roiba Journal, vol. 6, no. 8, Aug. 2024, doi: 10.47467/reslaj.v6i8.4852.
A. Farley and H. Ham, “Real time IP camera parking occupancy detection using deep learning,” Procedia Comput Sci, vol. 179, pp. 606–614, 2021, doi: 10.1016/j.procs.2021.01.046.
E. Dragonas, C. Lambrinoudakis, and M. Kotsis, “IoT forensics: Exploiting unexplored log records from the HIKVISION file system,” J Forensic Sci, vol. 68, no. 6, pp. 2002–2011, 2023, Accessed: May 07, 2025. [Online]. Available: https://doi.org/10.1111/1556-4029.15349
J. G. Stadler, K. Donlon, J. D. Siewert, T. Franken, and N. E. Lewis, “Improving the efficiency and ease of healthcare analysis through use of data visualization dashboards,” Big Data, vol. 4, no. 2, pp. 129–135, 2016, doi: 10.1089/big.2015.0059.
C. Y. Chen, S. H. Wu, B. W. Huang, C. H. Huang, and C. F. Yang, “Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform,” Internet of Things, vol. 27, p. 101305, Oct. 2024, doi: 10.1016/J.IOT.2024.101305.
W. Hamdy, A. Al-Awamry, and N. Mostafa, “Warehousing 4.0: A proposed system of using node-red for applying internet of things in warehousing,” Sustainable Futures, vol. 4, p. 100069, Jan. 2022, doi: 10.1016/J.SFTR.2022.100069.
Y. Bandung, Y. F. Hendra, and L. B. Subekti, “Design and implementation of digital signage system based on Raspberry Pi 2 for e-tourism in Indonesia,” in 2015 International Conference on Information Technology Systems and Innovation (ICITSI), IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/ICITSI.2015.7437699.
E. Noyes, J. P. Davis, N. Petrov, K. L. H. Gray, and K. L. Ritchie, “The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers,” R Soc Open Sci, vol. 8, no. 3, p. rsos.201169, Mar. 2021, doi: 10.1098/rsos.201169.
N. H. Lestriandoko, R. Veldhuis, and L. Spreeuwers, “The contribution of different face parts to deep face recognition,” Front Comput Sci, vol. 4, Aug. 2022, doi: 10.3389/fcomp.2022.958629.
W. Jones, J. D. Dinneen, R. Capra, A. Diekema, and M. Pérez-Quiñones, “Personal information management (PIM),” Encyclopedia of library and information science, pp. 3584–3605, 2017, doi: 10.1081/e-elis4-120053695.
I. Fourie, “Librarians alert: How can we exploit what is happening with personal information management (PIM), reference management and related issues?,” Library Hi Tech, vol. 29, no. 3, pp. 550–556, 2011, doi: 10.1108/07378831111174477.
W. P. Jones and J. Teevan, Personal information management, vol. 14. University of Washington Press Seattle, WA, 2007. doi: 10.1515/9780295800684.
A. Lush, “Fundamental personal information management activities–organisation, finding and keeping: A literature review,” Aust Libr J, vol. 63, no. 1, pp. 45–51, 2014, doi: 10.1080/00049670.2013.875452.
D. Elsweiler and I. Ruthven, “Towards task-based personal information management evaluations,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 23–30. doi: 10.1145/1277741.1277748.
E. A. Yani and A. M. Syam, “Implementasi Personal Information Management (PIM) Mahasiswa Tingkat Akhir Pada Program Studi Ilmu Perpustakaan,” Reslaj: Religion Education Social Laa Roiba Journal, vol. 6, no. 8, Aug. 2024, doi: 10.47467/reslaj.v6i8.4852.
A. Farley and H. Ham, “Real time IP camera parking occupancy detection using deep learning,” Procedia Comput Sci, vol. 179, pp. 606–614, 2021, doi: 10.1016/j.procs.2021.01.046.
E. Dragonas, C. Lambrinoudakis, and M. Kotsis, “IoT forensics: Exploiting unexplored log records from the HIKVISION file system,” J Forensic Sci, vol. 68, no. 6, pp. 2002–2011, 2023, Accessed: May 07, 2025. [Online]. Available: https://doi.org/10.1111/1556-4029.15349
J. G. Stadler, K. Donlon, J. D. Siewert, T. Franken, and N. E. Lewis, “Improving the efficiency and ease of healthcare analysis through use of data visualization dashboards,” Big Data, vol. 4, no. 2, pp. 129–135, 2016, doi: 10.1089/big.2015.0059.
C. Y. Chen, S. H. Wu, B. W. Huang, C. H. Huang, and C. F. Yang, “Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform,” Internet of Things, vol. 27, p. 101305, Oct. 2024, doi: 10.1016/J.IOT.2024.101305.
W. Hamdy, A. Al-Awamry, and N. Mostafa, “Warehousing 4.0: A proposed system of using node-red for applying internet of things in warehousing,” Sustainable Futures, vol. 4, p. 100069, Jan. 2022, doi: 10.1016/J.SFTR.2022.100069.
Y. Bandung, Y. F. Hendra, and L. B. Subekti, “Design and implementation of digital signage system based on Raspberry Pi 2 for e-tourism in Indonesia,” in 2015 International Conference on Information Technology Systems and Innovation (ICITSI), IEEE, Nov. 2015, pp. 1–6. doi: 10.1109/ICITSI.2015.7437699.
E. Noyes, J. P. Davis, N. Petrov, K. L. H. Gray, and K. L. Ritchie, “The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers,” R Soc Open Sci, vol. 8, no. 3, p. rsos.201169, Mar. 2021, doi: 10.1098/rsos.201169.
N. H. Lestriandoko, R. Veldhuis, and L. Spreeuwers, “The contribution of different face parts to deep face recognition,” Front Comput Sci, vol. 4, Aug. 2022, doi: 10.3389/fcomp.2022.958629.
M. Sheen, H. Aman Key Yekani, and T. R. Jordan, “Investigating the effect of wearing the hijab: Perception of facial attractiveness by Emirati Muslim women living in their native Muslim country,” PLoS One, vol. 13, no. 10, p. e0199537, Oct. 2018, doi: 10.1371/journal.pone.0199537.
L. Li, X. Mu, S. Li, and H. Peng, “A review of face recognition technology,” IEEE access, vol. 8, pp. 139110–139120, 2020, doi: 10.1109/access.2020.3011028.
I. D. Raji and G. Fried, “About face: A survey of facial recognition evaluation,” arXiv preprint arXiv:2102.00813, 2021.
D. J. Carragher and P. J. B. Hancock, “Surgical face masks impair human face matching performance for familiar and unfamiliar faces,” Cogn Res Princ Implic, vol. 5, no. 1, p. 59, Dec. 2020, doi: 10.1186/s41235-020-00258-x.
M. Wang and W. Deng, “Deep Face Recognition: A Survey,” Aug. 2020, doi: 10.1016/j.neucom.2020.10.081.
W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition,” ACM Comput Surv, vol. 35, no. 4, pp. 399–458, Dec. 2003, doi: 10.1145/954339.954342.
A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans Pattern Anal Mach Intell, vol. 23, no. 6, pp. 643–660, Jun. 2001, doi: 10.1109/34.927464.