A Check on WHO Protocol Implementation for COVID-19 Using IoT

Sharma, Abhinav; Dhingra, Jayant; Dawar, Parul

doi:10.1007/978-3-030-77528-5_3

Cited by 2 publications

(2 citation statements)

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“…Singh et al [32] employed MobileNetV2 to determine the mask's presence. Sharma et al [33] combined infrared temperature sensors with a CNN and used the RMFD dataset to detect masked faces. In [34], the authors used MobileNetV2 to determine mask presence.…”

Section: Related Workmentioning

confidence: 99%

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Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Alhammad,

Khafaga,

Hamed

et al. 2023

CSSE

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Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the realworld masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performance metrics for the proposed technique were measured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

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Section: Related Workmentioning

confidence: 99%

“…Our experiment used 755 images for masked faces and 754 for unmasked faces. The same subgroup was introduced in [33].…”

Section: Datasetsmentioning

confidence: 99%