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Volume 14
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10.3390/jlpea14030046
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Article

IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400

by
Ahmad Saeed Mohammad
1,
Thoalfeqar G. Jarullah
1,
Musab T. S. Al-Kaltakchi
2,
Jabir Alshehabi Al-Ani
3 and
Somdip Dey
3,4,*
1
Department of Computer Engineering, College of Engineering, Mustansiriyah University, Baghdad 10047, Iraq
2
Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad 10047, Iraq
3
Department of Data Science, York St. John University, York YO31 7EL, UK
4
Nosh Technologies, 14 Miranda Walk, Colchester, Colchester CO4 3SL, UK
*
Author to whom correspondence should be addressed.
J. Low Power Electron. Appl. 2024, 14(3), 46; https://doi.org/10.3390/jlpea14030046
Submission received: 6 August 2024 / Revised: 27 August 2024 / Accepted: 30 August 2024 / Published: 5 September 2024
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Abstract

IoT applications revolutionize industries by enhancing operations, enabling data-driven decisions, and fostering innovation. This study explores the growing potential of IoT-based facial recognition for mobile devices, a technology rapidly advancing within the interconnected IoT landscape. The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. Additionally, an in-house database is compiled, capturing data from 50 individuals via a web camera and 10 subjects through a smartphone camera. Pre-processing of the in-house database involves face detection using OpenCV’s Haar Cascade, Dlib’s CNN Face Detector, and Mediapipe’s Face. The resulting system demonstrates high accuracy in real-time and operates efficiently on low-powered devices like the Raspberry Pi 400. The evaluation involves the use of the multilayer perceptron (MLP) and support vector machine (SVM) classifiers. The system primarily functions as a closed set identification system within a computer engineering department at the College of Engineering, Mustansiriyah University, Iraq, allowing access exclusively to department staff for the department rapporteur room. The proposed system undergoes successful testing, achieving a maximum accuracy rate of 99.976%.
Keywords: Internet-of-Things applications; MobileNetV2; FaceNet; deep learning; Raspberry Pi type-400; facial identification; mobile application Internet-of-Things applications; MobileNetV2; FaceNet; deep learning; Raspberry Pi type-400; facial identification; mobile application

Share and Cite

MDPI and ACS Style

Mohammad, A.S.; Jarullah, T.G.; Al-Kaltakchi, M.T.S.; Alshehabi Al-Ani, J.; Dey, S. IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400. J. Low Power Electron. Appl. 2024, 14, 46. https://doi.org/10.3390/jlpea14030046

AMA Style

Mohammad AS, Jarullah TG, Al-Kaltakchi MTS, Alshehabi Al-Ani J, Dey S. IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400. Journal of Low Power Electronics and Applications. 2024; 14(3):46. https://doi.org/10.3390/jlpea14030046

Chicago/Turabian Style

Mohammad, Ahmad Saeed, Thoalfeqar G. Jarullah, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani, and Somdip Dey. 2024. "IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400" Journal of Low Power Electronics and Applications 14, no. 3: 46. https://doi.org/10.3390/jlpea14030046

APA Style

Mohammad, A. S., Jarullah, T. G., Al-Kaltakchi, M. T. S., Alshehabi Al-Ani, J., & Dey, S. (2024). IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400. Journal of Low Power Electronics and Applications, 14(3), 46. https://doi.org/10.3390/jlpea14030046

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