Masked Face Recognition using Deep Learning Model

Kumar, Manoj; Mann, Rachit

doi:10.1109/icac3n53548.2021.9725368

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Many systems have been constructed and developed for the sake of the importance of masked face recognition in different applications, the earliest proposed systems have been illustrated [21] and briefly discussed the time cost and accuracy of them. Some of these systems focused on the features extraction techniques and others are reliable on the deep learning model implemented.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based masked face recognition in the era of the COVID-19 pandemic

Abdulmunem

Al-Shakarchy

Safoq

2023

IJECE

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<span lang="EN-US">During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models.</span>

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Section: Introductionmentioning

confidence: 99%

Deep learning based masked face recognition in the era of the COVID-19 pandemic

Abdulmunem

Al-Shakarchy

Safoq

2023

IJECE

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Hands-Free Eye Gesture Authentication Using Deep Learning and Computer Vision Principles

Rohith

Muthuswamy

2023

Algorithms for Intelligent Systems

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Masked Face Recognition Using Convolutional Neural Networks

Abass

2023

Jour. Kufa Math. Comp.

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Since the COVID-19 epidemic's rise in 2020, Cover face recognize achieve advanced significantly in the range of computer vision. Face cover is important to stop or limit the COVID-19 disease's spread due to the global outbreak. Face recognize is among of the most commonly used biometric recognition approach, because it can beutilized for monitoring systems, identity management, security verifying, and a lot of applications. The majority features of faces were hidden by mask, leaving just a quite some, including eyes plus head-region, that’s utilized for recognize. This challenge may reduce the recognition percentage because of the limited area to extract features. Due to the popularity of deep learning to extract and recognize deep features in many research areas especially computer vision,In this work, a covered face recognize system is introduced. utilizing Convolutional neural network (CNN), one of the most widely common deep learning algorithms. The final layer in the CNN architecture, the softmax activation function, was utilized to identify the facial characteristics after they had been extracted using CNN from the masked face's eyes, forehead, and brow regions. In the Study employ the "Extended Yale B database," which has issues with changes in placement and lighting. additionally, they covered faces in Dataset with medical masks. In comparison to other approaches to solving this problem, our strategy showed to be successful and promising with a recognition accuracy for "Extended Yale B" of 95%.

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Masked Face Recognition using Deep Learning Model

Cited by 3 publications

References 15 publications

Deep learning based masked face recognition in the era of the COVID-19 pandemic

Deep learning based masked face recognition in the era of the COVID-19 pandemic

Hands-Free Eye Gesture Authentication Using Deep Learning and Computer Vision Principles

Masked Face Recognition Using Convolutional Neural Networks

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