Attendance monitoring of masked faces using ResNext-101

Mahapatra, Sushil Kumar; Pattanayak, Binod Kumar; Pati, Bibudhendu

doi:10.47974/JSMS-952

Open Access Research Article

Attendance monitoring of masked faces using ResNext-101

^*Sushil Kumar MahapatraCorresponding authormohapatrasushil@gmail.comDepartment of Computer Science & Engineering Siksha ‘O’ Anusandhan (Deemed to be Univesity) Bhubaneswar IndiaView full profile → , Binod Kumar Pattanayakbinodpattanayak@soa.ac.inDepartment of Computer Science & Engineering Siksha ‘O’ Anusandhan (Deemed to be Univesity) Bhubaneswar IndiaDepartment of Computer Science & Engineering Siksha O Anusandhan (Deemed to be University)Bhubaneswar, Odisha, IndiaView full profile → , Bibudhendu Patipatibibudhendu@gmail.comDepartment of Computer Science Ramadevi Women’s University Bhubaneswar IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 117–131Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-952XML

Published Online:: 31 Dec 2022
Article type:: Research Article
Language:: EN
Article no.:: JSMS-952
Pages:: 117–131

Abstract

SARC virus, Coronavirus, Ebola and bird flu have all caused pandemics in the last few decades. Most of these diseases spread through the air when someone coughs, sneezes or even talks. The government makes citizens wear masks. Furthermore, all academic activities are conducted in virtual mode as a result of this predicament, making taking attendance of pupils difficult while they are wearing masks on their faces. To overcome this issue, the proposed work will track a student’s attendance while using ResNext-101 in a virtual classroom setting. ResNext-101, a deep learning technique, is used on masked faces in this work and it is a good model for accurately detecting masked faces. By using the Gaussian data augmentation approach, the outcome reveals a level of accuracy of 51.70 percent with a loss of 1.9452.

Keywords

Attendance monitoring Masked face Resnet-50 ResNext-101 Teaching learning method Deep learning

Subject Classifications

68T0768U10

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