IoT based Novel Face Detection Scheme using Machine Learning Scheme

Sumathi, K.; Sakthi, D. Vishnu; Nirmala, G; Sellamuthu, P.; Walia, Ranjan; Usman, Mohd

doi:10.1109/accai53970.2022.9752504

Cited by 3 publications

(1 citation statement)

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“…Face detection is one of the most important operations in the field of digital image processing, which is used in many industries to solve security problems and save manual search time. Sumathi et al [22] proposed a unique face recognition paradigm that utilizes the IoT security environment to identify specific criminal behaviors. Sistu et al [23] proposed a framework for smartphone activation of secure access to closed indoor environments.…”

Section: Iot Face Recognition In a Cloud Environmentmentioning

confidence: 99%

A model study of teaching method reform of computer laboratory course integrating internet of things technology

Zhou,

Qian,

Aziz

et al. 2024

PLoS ONE

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The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

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Section: Iot Face Recognition In a Cloud Environmentmentioning

confidence: 99%