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

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 inves...

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Bibliographic Details
Published in:Journal of low power electronics and applications Vol. 14; no. 3; p. 46
Main Authors: Mohammad, Ahmad Saeed, Jarullah, Thoalfeqar G., Al-Kaltakchi, Musab T. S., Alshehabi Al-Ani, Jabir, Dey, Somdip
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2024
Subjects:
Accuracy
Algorithms
Artificial intelligence
Big Data
Biometry
Computational linguistics
Computer vision
Deep learning
Electronic health records
Energy consumption
Equipment and supplies
Face recognition
FaceNet
facial identification
Facial recognition technology
Identification
Information management
Innovations
Internet of Things
Internet-of-Things applications
Language processing
Machine learning
MobileNetV2
Multilayer perceptrons
Natural language interfaces
Neural networks
Optimization techniques
Raspberry Pi type-400
Real time
Smart phones
Smartphones
Software
Support vector machines
Surveillance
Technology application
User experience
United Kingdom
Iraq
ISSN:2079-9268, 2079-9268
Online Access:Get full text
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Summary: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%.
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content type line 14
ISSN:2079-9268
2079-9268
DOI:10.3390/jlpea14030046