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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Veröffentlicht in:	Journal of low power electronics and applications Jg. 14; H. 3; S. 46
Hauptverfasser:	Mohammad, Ahmad Saeed, Jarullah, Thoalfeqar G., Al-Kaltakchi, Musab T. S., Alshehabi Al-Ani, Jabir, Dey, Somdip
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.09.2024
Schlagworte:	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
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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%.
AbstractList	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%.
Audience	Academic
Author	Dey, Somdip Alshehabi Al-Ani, Jabir Mohammad, Ahmad Saeed Al-Kaltakchi, Musab T. S. Jarullah, Thoalfeqar G.
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SubjectTerms	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
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Title	IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400
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