TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Facial authentication-based smart door lock system and anomaly detection using machine learning architectures integrated with IoT

* , , ,

* Corresponding author · click or hover a name for details

pp. 1901–1909Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2019XML
Received:
10 Dec 2024
Published Online:
30 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2019
Pages:
1901–1909

Abstract

This work comprises two components: home security and reconnaissance, as well as remote entryway exchange with a greeting framework.  The first step for the user is to install the security and surveillance hardware components of the system.  A growing number of people are considering installing smart home systems as a result of the proliferation of IoT devices.  Security and access control are important components of the smart home system.  This paper details the development of a facial recognition security framework that makes use of Raspberry Pi, which is regularly integrated with smart home systems.  An SDL system that relies on face authentication and intrusion detection is the goal of this project, which intends to use machine learning architectures and the internet of things (IoT).  To secure this smart door, biometric authentication based on face recognition is utilized.  Next, we employ the DeSGCBQNN, which stands for distributed encoder Shannon-Gaussian Correlation Bayesian Q-neural networks, to spot outliers.  We completed the trial examination for several smart entrance face datasets in terms of mean square error, false acceptance rate, false rejection rate, mean average precision, and accuracy.  The proposed approach achieved a 98% success rate, a 66% mean average precision, a 65% false acceptance rate, a 55% false rejection rate, and a 53% mean square error.

Keywords

Subject Classifications

68T1068T4594A62

References

[1] T. K. Mohammed, D. N. Vasundhara, S. H. Mehanoor, E. Sreedevi, P. R. Kumar, C. Manihass, and S. F. Baba, “A novel fusion approach for advancement in crime prediction and forecasting using hybridization of ARIMA and recurrent neural networks,” Journal of Information Systems Engineering and Management, vol. 10, no. 38, pp. 404–420 (2025).
[2] M. Hanif, U. Shahzad, I. Shahzadi, and N. Koyuncu, “Stochastically increasing grouped data using the MLE of mean of the generalized exponential distribution,” TARU J. Organ. Behav. & Analytics, vol. 1, no. 1, pp. 65–82 (2019).
[3] D. Goyal, A. Kumar, and D. S. Jat, “Special issue on advancements in intelligent systems for information optimization in cyber security: Challenges and innovations (Part-B),” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-B, pp. i–viii (2025).
[4] H.-C. Cheng, M.-C. Chiu, and C.-M. Chiu, “A lighting system using multiple climate sensors in conjunction with remote monitoring network,” TARU J. Comput. Dyn. Technol. Adv., vol. 1, no. 1, pp. 21–38 (2019).
[5] S. Sharma, A. K. Patel, and N. Menaria, “Integral transform of product of generalized k-Bessel function and generalized Hurwitz-Lerch Zeta function,” Journal of Interdisciplinary Mathematics, vol. 27, no. 8, pp. 1757–1764 (2024).
[6] B. Shilpa, P. R. Kumar, and R. K. Jha, “Spreading factor optimization for interference mitigation in dense indoor LoRa networks,” in Proc. IEEE IAS Glob. Conf. Emerg. Technol. (GlobConET), pp. 1–5 (2023).
[7] P. R. Kumar and B. Shilpa, “An IoT-based smart healthcare system with edge intelligence computing,” in Reconnoitering the Landscape of Edge Intelligence in Healthcare, CRC Press, pp. 31–46 (2024).
[8] P. Dass, V. Gupta, S. Dhingra, R. Arora, P. Katariya, and A. Kumar, “A deep learning model to predict and classify crime rate using tweets,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 8, pp. 2255–2271 (2024).
[9] G. B. Mohammad, Shitharth, and P. R. Kumar, “Integrated machine learning model for URL phishing detection,” International Journal of Grid and Distributed Computing, vol. 14, no. 1, pp. 513–529 (2021).

Views: 220Downloads: 96Citations: 0