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2022
DOI: 10.11591/ijeecs.v29.i1.pp304-314
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Increasing validation accuracy of a face mask detection by new deep learning model-based classification

Abstract: During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at t… Show more

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Cited by 5 publications
(3 citation statements)
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“…Generally, there has been a lot of focus on computation cost [ 30 , 31 ]. It is considered as a key element which assist in ill-conditioning.…”
Section: Introductionmentioning
confidence: 99%
“…Generally, there has been a lot of focus on computation cost [ 30 , 31 ]. It is considered as a key element which assist in ill-conditioning.…”
Section: Introductionmentioning
confidence: 99%
“…The suggested deep model uses less computing time without compromising accuracy. A volume of input is divided into several volumes in the model, and a tree is generated for each volume (Masud et al, 2020;Joodi, 2023). Facial recognition and cloud-based mobile edge computing are suggested to provide an immersive online biometric authentication method for online guiding.…”
Section: Introductionmentioning
confidence: 99%
“…Several neural network classifers are applied to the Twitter-collected dataset of people's faces. Human accuracy is just 26.96%, whereas the best accuracy attained is 53.2% [7,8]. A linear SVM classifer and a multilabel, deep learning-based facial action detector beat cutting-edge methods (HOG and LBP), are used.…”
Section: Introductionmentioning
confidence: 99%