Smart Hostel Management System with Facial Recognition and Predictive Behavioural Analytics
DOI:
https://doi.org/10.56532/mjsat.v6i3.697Keywords:
Hostel Management System, Facial Recognition, Predictive Analytics, DeepFace, Decision TreeAbstract
The higher education sector in Malaysia is expanding rapidly, aiming to attract 250,000 international students by 2025. With this rapid growth, university hostels require a more efficient management system to ensure the safety and well-being of residents. However, many hostels still rely on paper-based logs or basic RFID systems. These traditional methods are prone to human error and are at risk of proxy attendance. Furthermore, these systems are often passive and fail to analyse resident data to identify behavioural risks. The aim of this project was to investigate these limitations and develop a Smart Hostel Management System that integrates biometric authentication using the DeepFace framework and predictive analytics. The specific objective was to build a centralized platform that automates entry/exit logging to eliminate identity spoofing and utilize the Decision Tree machine learning model to predict resident risk levels based on behavioural history. The research proved that integrating Convolutional Neural Networks (CNN) with decision-based learning can effectively replace manual monitoring.
References
A. Walker, “Malaysia on track to attract 250,000 foreign students by 2025,” GSL Global, February 14, 2025. url: https://gslglobal.com/2025/02/14/malaysia-on-track-to-attract-250000-foreign-students-by-2025/
PowerSchool, “The problem with using spreadsheets for K–12 data analytics,” January 2024, url: https://www.powerschool.com/blog/the-problem-with-using-spreadsheets-for-k-12-data-analytics/#:~:text=But%20a%20spreadsheet%20is%20limited,cells%2C%20rows%2C%20and%20columns.
M. Rebuglio, F. M. Ottaviani, and A. D. Marco, “Recordkeeping for project management information system in public procurement: An action research,” Records Management Journal, February 2025, doi: https://doi.org/10.1108/RMJ-10-2023-0052.
M. Al-Dabbagh, “A review of students attendance management systems,” Eurasian Journal of Science and Engineering, vol. 10, no. 3, pp. 136–145, April 2025, doi: https://doi.org/10.23918/eajse.v10i3p13.
M. S. I. Shovon, “Security and Privacy Issues Related to Electronic Documents based on the Public Perception,” Journal of Information Education Research, vol. 5, no. 4, pp. 1678-1686, December 2025, Available: https://jier.org/index.php/journal/article/view/3954
K. A. Barchard and L. A. Pace, “Preventing human error: The impact of data entry methods on data accuracy and statistical results,” Comput. Hum. Behav., vol. 27, no. 5, pp. 1834–1839, September 2011, doi: https://doi.org/10.1016/j.chb.2011.04.004.
R. Rao, “Radio Frequency Identification,” International Journal of Innovative Research in Advanced Engineering, vol. 09, no. 12, pp. 489–492, December 2022.
M. I. M. Rozlan et al., “RFID-based attendance monitoring system with LED authentication,” in Proceeding 2023 IEEE International Confrence on Automatic Control and Intelligent System (I2CACIS), June 2023, pp. 85–90, doi: https://doi.org/10.1109/I2CACIS57635.2023.10193394.
M. Imran, M. Fikry, and S. Retno, “Enhancing academic security with RFID-based smart locks and real-time attendance tracking system,” in Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS), vol. 4, December 2024, doi: https://doi.org/10.29103/micoms.v4i.950.
H. Pereira, R. Carreira, P. Pinto, and S. I. Lopes, “Hacking the RFID-based authentication system of a university campus on a budget,” in Proceedings Iberian Conference on Information Systems and Technologies (CISTI), 2020, pp. 1–5, doi: https://doi.org/10.23919/CISTI49556.2020.9140943.
Y. Wang et al., “A Protocol Stack for Large-Scale RFID Systems: Mitigating Reader and Tag Collisions,” IEEE Internet of Things Journal, vol. 11, no. 19, pp. 30455-30468, May 2024, doi: https://doi.org/10.1109/JIOT.2024.3398650.
A. A. Tamimi, O. N. Al-Allaf, and M. A. Alia, “Eigen faces and principle component analysis for face recognition systems: A comparative study,” International Journal of Computer amd Technology, vol. 14, no. 4, pp. 5650–5660, February 2015, doi: https://doi.org/10.24297/ijct.v14i4.1967.
A. K. Singh, S. Kumar, B. Kumar, and A. S. Chauhan, “Face Recognition Using Machine Learning,” in 2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), pp. 361–364, September 2023, doi: https://doi.org/10.1109/icseiet58677.2023.10303390.
V. K. Kulamala et al., “Non-intrusive facial recording system with convolution neural network (cnn) for attendance assimilation,” May 2024, pp. 130–150, doi: https://doi.org/10.58532/v3baai7p1ch9.
L. Chen et al., “Review of image classification algorithms based on convolutional neural networks,” Remote Sens, vol. 13, no. 22, p. 4712, November 2021, doi: https://doi.org/10.3390/rs13224712.
D. Saez-Trigueros, L. Meng, and M. Hartnett, “Face recognition: From traditional to deep learning methods,” arXiv, October 2018, doi: https://doi.org/10.48550/arXiv.1811.00116.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 815–823, doi: https://doi.org/10.1109/CVPR.2015.7298682.
E. Solomon and K. J. Cios, “FASS: Face anti-spoofing system using image quality features and deep learning,” Electronics, vol. 12, no. 10, p. 2199, May 2023, doi: https://doi.org/10.3390/electronics12102199.
C. Widjaya and A. Wicaksana, “Liveness detection with randomized challenge-response for face recognition anti-spoofing,” International Journal of Innovative Computing, Information and Control, vol. 19, no. 2, pp. 419–430, April 2023, doi: https://doi.org/10.24507/ijicic.19.02.419.
A. Koschmider and S. Speidel, “Predictive behavior analysis for smart environments,” Emisa Forum, vol. 36, pp. 79–82, October 2016.
D. Abbott, Applied predictive analytics: Principles and techniques for the professional data analyst. Wiley, 2014.
P. Lourens and D. Bleazard, “Applying predictive analytics in identifying students at risk: A case study,” South African Journal of Higher Education., vol. 30, no. 3, June 2016, pp. 129-142, doi: https://doi.org/10.20853/30-2-583.
V. Matzavela and E. Alepis, “Decision tree learning through a predictive model for student academic performance in intelligent m-learning environments,” Computers and Education: Artificial Intelligence, vol. 2, Art. no. 100035, January 2021, doi: https://doi.org/10.1016/j.caeai.2021.100035.
D. Kumar, A. Kothiyal, R. Kumar, C. Hemantha and R. Maranan, “Random Forest approach optimized by the Grid Search process for predicting the dropout students,” in 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), pp. 1–6, Jun. 2024, doi: https://doi.org/10.1109/ICICET59348.2024.10616372.
A. Assis, J. Dantas, and E. Andrade, “The performance-interpretability trade-off: a comparative study of machine learning models,” Journal of Reliable Intelligent Environments, vol. 11, no. 1, Art. no. 1, December 2024, doi: https://doi.org/10.1007/s40860-024-00240-0.
A. Rai, “Explainable AI: from black box to glass box,” Journal of the Academy of Marketing Science, December 2020, doi: https://doi.org/10.1007/s11747-019-00710-5.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep Face Recognition,” in Proc. British Machine Vision Conference (BMVC), Swansea, UK, 2015, doi: https://doi.org/10.5244/C.29.41.
A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, Jan. 2004, doi: https://doi.org/10.1109/TCSVT.2003.818349.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 815–823, doi: https://doi.org/10.1109/CVPR.2015.7298682.
T. Soukupová and J. Čech, “Real-time eye blink detection using facial landmarks,” in Proc. 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, Feb. 2016, pp. 1–8.
V. S. K. Rupani, V. V. S. Thushar, and K. Tejith, “Real-time drowsiness detection using eye aspect ratio and facial landmark detection,” arXiv, arXiv:2408.05836, Aug. 2024, doi: https://doi.org/10.48550/arXiv.2408.05836.
A. Almheiri and Z. Maamar, “IoT Protocols – MQTT versus CoAP,” in Proceedings of the 4th International Conference on Networking, Information Systems & Security (NISS 2021), Association for Computing Machinery, 2021, doi: https://doi.org/10.1145/3454127.3456594.
Q. Li, H. Xiang, W. Cui, K. Liu, and D. Li, “A Multi-Factor Authentication Method for Power Grid Terminals Based on Edge Computing Paradigm,” IEEE Access, vol. 13, pp. 166062–166077, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3609692.
S. Mondal et al., “Most people obey arbitrary rules even when it’s not in their interest to do so,” Nature Human Behaviour, vol. 9, pp. 1342–1354, 2025.
I. M. Mohammed and N. A. Mat Isa, “Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement,” IEEE Access, vol. 13, pp. 62600–62632, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3558506.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Goh Qin Nee, Justtina Anantha Jothi John
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
