Masked face detection and selected employee access to workplaces: a step towards coronavirus prevention (Record no. 20785)

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control field 20240319120222.0
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Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
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9 (RLIN) 23070
Author Mandal, Sujit
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Title Masked face detection and selected employee access to workplaces: a step towards coronavirus prevention
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Volume, Issue number Vol.104(6), Dec
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. USA
Name of publisher, distributor, etc. Springer
Year 2023
300 ## - PHYSICAL DESCRIPTION
Pagination 1353-1368p.
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Summary, etc. When the coronavirus surged to a record high, the number of employees entering an organization was restricted. It was essential to monitor the entry of selected employees with facial masks at an organization. In this work, we propose a model using CCTV (closed circuit television) camera to check whether an incoming person to an organization is wearing a facial mask or not. If so, our model further checks whether the person is genuinely a selected employee of the organization. Accordingly, the entrance to the organization is either opened or closed. A two tier convolutional neural network (CNN) is implemented here. A new technique to train CNN is designed using Haar (wavelet) classifier for object detection. The first tier CNN detects only masked facial images with an accuracy of 98.67%. The second tier CNN detects masked selected employees with an accuracy of 99%. The results obtained are better than those of contemporary works. In the future a hardware implementation of the model is suggested.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4642
Topical term or geographic name entry element Humanities and Applied Sciences
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9 (RLIN) 23071
Co-Author Saha, Manas
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International Standard Serial Number 2250-2106
Title Journal of the institution of engineers (India): Series B
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URL https://link.springer.com/article/10.1007/s40031-023-00945-5
Link text Click here
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Koha item type Articles Abstract Database
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          School of Engineering & Technology School of Engineering & Technology Archieval Section 2024-03-19 2024-0306 2024-03-19 2024-03-19 Articles Abstract Database
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