Evaluation of two biometric access control systems using the Susceptible-Infected-Recovered model

Bopatriciat BOLUMA MANGATA; Odette Sangupamba Mwilu; Patience Ryan Tebua Tene; Gilgen Mate Landry

doi:10.35882/jeemi.v5i2.288

Bopatriciat BOLUMA MANGATA Section d’Environnement et Développement Durable, Institut Supérieur des Etudes Agronomiques de KIYAKA-GUNGU, Gungu, Democratic Republic of Congo https://orcid.org/0000-0003-4198-6809
Odette Sangupamba Mwilu Faculty of Computer Science, Université Catholique du Congo, Kinshasa, Democratic Republic of Congo https://orcid.org/0000-0001-9094-0626
Patience Ryan Tebua Tene Faculty of Computer Science, Université Catholique du Congo Kinshasa, Democratic Republic of Congo https://orcid.org/0009-0007-2828-0314
Gilgen Mate Landry Centre de Recherche Interdisciplinaire de l’Université Pédagogique Nationale, Université Pédagogique Nationale, Kinshasa, Democratic Republic of Congo https://orcid.org/0009-0003-4729-0973

DOI: https://doi.org/10.35882/jeemi.v5i2.288

Keywords: access control, biometrics, Euler method for system of tree ODEs, Euler method for SIR model, pattern recognition, SIR model

Abstract

This paper evaluates the effectiveness of decisions made on two single-mode biometric systems based on facial recognition and fingerprints for access control. To achieve this, we first implemented an embedded system under Arduino to allow us to open and close doors, then we programmed two biometric recognition systems, namely facial recognition and fingerprint recognition, and finally we exploited the Susceptible-Infected-Covered model without demographics to evaluate the efficiency of these two access control systems. The variables used in the analysis were the number of individuals enrolled in the biometric system to be subject to access control (Susceptible), the number of individuals enrolled in the biometric system and denied access by the system, as well as the number of individuals not enrolled in the biometric system and allowed access by the system (Infected), and the number of false acceptance rates and false rejection rates at time t in the systems (Retrieved). In a sample of 600 individuals, of which 300 were enrolled and 300 were not, our two simple modal access control systems each obtained the following results: 270 true positives, 30 false negatives, 48 false positives and 252 true negatives for the facial recognition system, compared to 288 true positives, 12 false negatives, 24 false positives and 276 true negatives for the fingerprint recognition system, which constitute our confusion matrix. Based on this confusion matrix, we were able to exploit the false rejection rates and false acceptance rates to correct for these inconveniences using the SIR model, i.e. 78 infected individuals for the facial recognition system, compared to 36 infected individuals for the fingerprint recognition system over a period of 216 days. The results show that the fingerprint recognition system is more efficient than the facial recognition system, according to the SIR model without demographic formulation.

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References

Aliouche, S., & Chetibi, H. E. (2021). Reconnaissance biométrique des personnes par la caractérisation de la rétine (Doctoral dissertation, Université jijel).

Bagal, D. K., Rath, A., Barua, A., & Patnaik, D. (2020). Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods. Chaos, Solitons & Fractals, 140, 110154.

Bonazza, P. (2019). Système de sécurité biométrique multimodal par imagerie, dédié au contrôle d’accès (Doctoral dissertation, Bourgogne Franche-Comté).

Dhanwant, J. N., & Ramanathan, V. (2020). Forecasting covid 19 growth in india using susceptible-infected-recovered (sir) model. arXiv preprint arXiv:2004.00696.

Duan, H., & Nie, W. (2022). A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19. Physica A: Statistical Mechanics and its Applications, 602, 127622.

Forgács, P., Libál, A., Reichhardt, C., Hengartner, N., & Reichhardt, C. J. O. (2022). Using active matter to introduce spatial heterogeneity to the susceptible infected recovered model of epidemic spreading. Scientific Reports, 12(1), 11229.

Gurova, S. M., Gurov, T., & Karaivanova, A. (2020, December). Scalability study of MPI algorithm for a predator-prey model with SEIRS epidemic disease. In AIP Conference Proceedings (Vol. 2302, No. 1, p. 030001). AIP Publishing LLC.

Hassan, S. A., Teoh, Y. K., Nasir, D. S. M., & Sharil, N. S. (2022). Simulation of COVID-19 Trend in Selangor via SIR Model of Infectious Disease. Journal of Computing Research and Innovation, 7(2), 294-303.

Kahina, O., & Ania, Z. (2019). Contrôle d’accès à base d’empreinte digitale (Doctoral dissertation, Université Mouloud Mammeri).

Liu, R., Pisco, A. O., Braun, E., Linnarsson, S., & Zou, J. (2022). Dynamical systems model of RNA velocity improves inference of single-cell trajectory, pseudo-time and gene regulation. Journal of Molecular Biology, 434(15), 167606.

Mangata, B. B., Nakashama, D. I., Muamba, D. K., & Christian, P. B. (2022). Implementation of an access control system based on bimodal biometrics with fusion of global decisions: Application to facial recognition and fingerprints. Journal of Computing Research and Innovation, 7(2), 43-53.

Mangata, B. B., Muamba, K., Khalaba, F., Parfum, B. C., & Mbambi, K. (2022). Parallel and Distributed Computation of a Fingerprint Access Control System. Journal of Computing Research and Innovation, 7(2), 1-10.

Mangata, B. B., Mbambi, K., Muamba, K., & Khalaba, F. (2022). Modeling and implementation of an automatic Access control system for secure permises using facial recognition. Journal of Computing Research and Innovation, 7(2), 11-22.

Mangata, B. B., N’kashama, D. I., Christian, P. B., & Muamba, D. K. (2022). COMPARATIVE STUDIES BETWEEN A FACIAL RECOGNITION SYSTEM AND A FINGERPRINT RECOGNITION SYSTEM FOR ACCESS CONTROL. IJISCS (International Journal of Information System and Computer Science), 6(2), 69-75.

Mangata, B. B., Eugène, M. M., Nzambi, B. M., Maheshe, C. B., & Christian, P. B. (January 2022). PERFORMANCE EVALUATION OF A SINGLE-MODE BIOMETRIC ACCESS CONTROL SYSTEM. Journal of Research in Engineering and Applied Sciences, Volume 7, Issue 1, 270-275

Meng, Q., & Jiang, X. (2022, September). A Simple Numerical Solution Framework for Ordinary Differential Equations Based on Reduced MIPS Instructions. In 2022 IEEE 4th International Conference on Circuits and Systems (ICCS) (pp. 53-59). IEEE.

Saeedian, M., Khalighi, M., Azimi-Tafreshi, N., Jafari, G. R., & Ausloos, M. (2017). Memory effects on epidemic evolution: The susceptible-infected-recovered epidemic model. Physical Review E, 95(2), 022409.

Sharif, N., Bidin, J., Ku Akil, K. A., & Mazlan, S. F. (2022). The effectiveness of online video marketing on Facebook using Susceptible-Infected-Recovered (SIR) model. Journal of Computing Research and Innovation (JCRINN), 7(2), 54-65.

Shoaib Arif, M., Raza, A., Abodayeh, K., Rafiq, M., Bibi, M., & Nazeer, A. (2020). A numerical efficient technique for the solution of susceptible infected recovered epidemic model. Computer Modeling in Engineering & Sciences, 124(2), 477-491.

Srivastava, H. M., & Günerhan, H. (2019). Analytical and approximate solutions of fractional-order susceptible-infected-recovered epidemic model of childhood disease. Mathematical Methods in the Applied Sciences, 42(3), 935-941.

Teoh, Y. K., Hamdan, N. F., Hasan, S. A., Ariffin, A. F., & Mahat, A. (2022). Application of Susceptible-Infected-Removed Model with Vital Dynamics for COVID-19 Outbreak in Malaysia. Journal of Computing Research and Innovation, 7(2), 82-87.

Televnoy, A., Ivanov, S., Zudilova, T., & Voitiuk, T. (2021, January). Neural ODE Machine Learning Method with Embedded Numerical Method. In 2021 28th Conference of Open Innovations Association (FRUCT) (pp. 451-457). IEEE.

Veeresha, P., Ilhan, E., Prakasha, D. G., Baskonus, H. M., & Gao, W. (2022). A new numerical investigation of fractional order susceptible-infected-recovered epidemic model of childhood disease. Alexandria Engineering Journal, 61(2), 1747-1756.

Wang, P., Li, Y., Yu, P., & Zhang, Y. (2021). The analysis of urban flood risk propagation based on the modified susceptible infected recovered model. Journal of Hydrology, 603, 127121.

Wang, W., Liu, Q. H., Zhong, L. F., Tang, M., Gao, H., & Stanley, H. E. (2016). Predicting the epidemic threshold of the susceptible-infected-recovered model. Scientific reports, 6(1), 1-12.