A New Smart Home Intruder Detection System Based on Deep Learning
DOI:
https://doi.org/10.23851/mjs.v34i2.1267Keywords:
Smart home, deep learning, efficientNetB4, transfer learningAbstract
The security of home doors has become one of the necessities in this era. The Internet of Things (IoT) technology has also entered into building the smart home. Therefore, it has become necessary to develop a facial recognition system that can be implemented on IoT devices. This study presented a method to recognize faces using the efficientnet-b4. Transfer learning with fine-tuning was used here due to the small dataset size and high accuracy (accuracy of Top-1= 82.9% and accuracy of Top-5 = 96.4%) of EfficientNet-B4 and it has fewer parameters (19.5 M) than the previously known model and this is what we are looking for in order to implement it on the Raspberry Pi. After training and saving the model, it is converted into a lightweight model and transferred to the Raspberry to distinguish faces. The results showed that the model had an accuracy of 97%, despite the fact that the collected images were taken in different lighting, different places, and different facial expressions.
Received: 29/11/2022
Accepted: 31/01/2023
Downloads
References
D. Marikyan, S. Papagiannidis, and E. Alamanos, "A systematic review of the smart home literature: A user perspective," Technol. Forecast. Soc. Change, vol. 138, no. June 2018, pp. 139-154, 2019.
K. Maswadi, N. B. A. Ghani, and S. B. Hamid, "Systematic Literature Review of Smart Home Monitoring Technologies Based on IoT for the Elderly," IEEE Access, vol. 8, pp. 92244-92261, 2020.
R. Khalaf, A. Mohammed, E. Essa, and H. Ali, "Controlling Smart Home Activities Using IoT," ICCISTA 2019 - IEEE Int. Conf. Comput. Inf. Sci. Technol. their Appl. 2019, pp. 1-6, 2019.
M. Lombardi, F. Pascale, and D. Santaniello, "Internet of things: A general overview between architectures, protocols and applications," Inf., vol. 12, no. 2, pp. 1-21, 2021.
P. Sethi and S. R. Sarangi, "Internet of Things: Architectures, Protocols, and Applications," J. Electr. Comput. Eng., vol. 2017, 2017.
A. H. Mohammed and R. M. A. Hussein, "Security Services for Internet of Thing Smart Health Care Solutions Based Blockchain Technology," Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 4, pp. 772-779, 2022.
H. K. Hoomod, J. R. Naif, and I. S. Ahmed, "A new intelligent hybrid encryption algorithm for IoT data based on modified PRESENT-Speck and novel 5D chaotic system," Period. Eng. Nat. Sci., vol. 8, no. 4, pp. 2333-2345, 2020.
V. C. Pathayapuram and A. A. Chikkamannur, "An Ameliorated Ensemble Approach for IoT Resource Feature Selection Based on Discriminating and Service Relevance Criteria," Int. J. Intell. Eng. Syst., vol. 14, no. 3, pp. 435-446, 2021.
C. Sisavath and L. Yu, "Design and implementation of security system for smart home based on IOT technology," Procedia Comput. Sci., vol. 183, pp. 4-13, 2021.
I. G. M. Ngurah Desnanjaya and I. N. A. Arsana, "Home security monitoring system with IoT-based Raspberry Pi," Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 3, pp. 1295-1302, 2021.
Z. M. Abood, G. S. Karam, and R. E. Haleot, "Face Recognition Using Fusion of Multispectral Imaging," 2017 2nd Al-Sadiq Int. Conf. Multidiscip. IT Commun. Sci. Appl. AIC-MITCSA 2017, pp. 107-112, 2017.
P. Annamalai, "Automatic face recognition using enhanced firefly optimization algorithm and deep belief network," Int. J. Intell. Eng. Syst., vol. 13, no. 5, pp. 19-28, 2020.
N. S. Irjanto and N. Surantha, "Home Security System with Face Recognition based on Convolutional Neural Network," Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 11, pp. 408-412, 2020.
G. Mubarak and E. Abdul Kareem, "In-Door Surveillance Module Based on an Associative Memory," AL-Rafidain J. Comput. Sci. Math., vol. 15, no. 2, pp. 13-26, 2021.
J. Patel, S. Anand, and R. Luthra, "Image-Based Smart Surveillance and Remote Door Lock Switching System for Homes," Procedia Comput. Sci., vol. 165, no. 2019, pp. 624-630, 2019.
R. A. Nadafa, S. M. Hatturea, V. M. Bonala, and S. P. Naikb, "Home Security against Human Intrusion using Raspberry Pi," in Procedia Computer Science, 2020, vol. 167, no. Iccids 2019, pp. 1811-1820.
O. Taiwo, A. E. Ezugwu, O. N. Oyelade, and M. S. Almutairi, "Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model," Wirel. Commun. Mob. Comput., vol. 2022, 2022.
K. A. Hussein and Z. T. A. Al-Ani, "Iraqi License Plate Recognition Based on Neural Network Technique," J. Phys. Conf. Ser., vol. 2322, no. 1, 2022.
S. Karkra, P. Singh, and K. Kaur, "Convolution neural network: A shallow dive in to deep neural net technology," Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 7, pp. 487-495, 2019.
M. A. Mazurowski, M. Buda, A. Saha, and M. R. Bashir, "Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI," J. Magn. Reson. Imaging, vol. 49, no. 4, pp. 939-954, 2019.
J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, "Deep learning in computer vision: A critical review of emerging techniques and application scenarios," Mach. Learn. with Appl., vol. 6, no. August, p. 100134, 2021.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, "Deep Learning for Computer Vision: A Brief Review," Comput. Intell. Neurosci., vol. 2018, 2018.
A. Thakkar and R. Lohiya, A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges, vol. 28, no. 4. Springer Netherlands, 2021.
L. Aversano, M. L. Bernardi, M. Cimitile, and R. Pecori, "A systematic review on Deep Learning approaches for IoT security," Comput. Sci. Rev., vol. 40, p. 100389, 2021.
D. Zhang, Z. Liu, and X. Shi, "Transfer learning on EfficientNet for remote sensing image classification," Proc. - 2020 5th Int. Conf. Mech. Control Comput. Eng. ICMCCE 2020, pp. 2255-2258, 2020.
S. Gang, N. Fabrice, D. Chung, and J. Lee, "Character recognition of components mounted on printed circuit board using deep learning," Sensors, vol. 21, no. 9, 2021.
A. W. Reza, M. M. Hasan, N. Nowrin, and M. M. Ahmed Shibly, "Pre-trained deep learning models in automatic COVID-19 diagnosis," Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 3, pp. 1540-1547, 2021.
X. Han et al., "Pre-trained models: Past, present and future," AI Open, vol. 2, no. August 2021, pp. 225-250, 2021.
K. S. Lee, E. Lee, B. Choi, and S. B. Pyun, "Automatic pharyngeal phase recognition in untrimmed videofluoroscopic swallowing study using transfer learning with deep convolutional neural networks," Diagnostics, vol. 11, no. 2, 2021.
L. Vogado et al., "Diagnosis of leukaemia in blood slides based on a fine-tuned and highly generalisable deep learning model," Sensors, vol. 21, no. 9, 2021.
A. S. Mahdi and S. A. Mahmood, "An Edge Computing Environment for Early Wildfire Detection," Ann. Emerg. Technol. Comput., vol. 6, no. 3, pp. 56-68, 2022.
K. B. Pranav and J. Manikandan, "Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks," Procedia Comput. Sci., vol. 171, no. 2019, pp. 1651-1659, 2020.
A. S. Sanchez-Moreno, J. Olivares-Mercado, A. Hernandez-Suarez, K. Toscano-Medina, G. Sanchez-Perez, and G. Benitez-Garcia, "Efficient face recognition system for operating in unconstrained environments," J. Imaging, vol. 7, no. 9, 2021.
Downloads
Key Dates
Published
Issue
Section
License
Copyright (c) 2023 Al-Mustansiriyah Journal of Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles accepted for publication in Al-Mustansiriyah Journal of Science (MJS) are protected under the Creative Commons Attribution 4.0 International License (CC BY-NC). Authors of accepted articles are requested to sign a copyright release form prior to their article being published. All authors must agree to the submission, sign copyright release forms, and agree to be included in any correspondence between MJS and the authors before submitting a work to MJS. For personal or educational use, permission is given without charge to print or create digital copies of all or portions of a MJS article. However, copies must not be produced or distributed for monetary gain. It is necessary to respect the copyright of any parts of this work that are not owned by MJS.
How to Cite
