Mohammad, Ahmad Saeed ORCID: https://orcid.org/0000-0001-6141-2605, Jarullah, Thoalfeqar G. ORCID: https://orcid.org/0009-0000-2548-9648, Al-Kaltakchi, Musab T. S. ORCID: https://orcid.org/0000-0001-5542-9144, Alshehabi Al-Ani, Jabir ORCID: https://orcid.org/0000-0002-0553-2538 and Dey, Somdip ORCID: https://orcid.org/0000-0001-6161-4637 (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). p. 46.

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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%.

Item Type:	Article
Status:	Published
DOI:	10.3390/jlpea14030046
School/Department:	School of Science, Technology and Health
URI:	https://ray.yorksj.ac.uk/id/eprint/10668

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