HybridFaceMaskNet: A Novel Face-Mask Detection Framework Using Hybrid Approach

Bhattacharya, Indrajit

doi:10.21203/rs.3.rs-476241/v1

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Based on conventional ML and DL-based techniques, Bhattacharya et al [17] developed a hybrid model for face mask recognition. This hybrid model trains an SVM, a combined algorithm, and a decision tree to classify images using the Resnet 50 feature extraction algorithm into masks and non-masks.…”

Section: Plos Onementioning

confidence: 99%

A hybrid human recognition framework using machine learning and deep neural networks

Sheneamer,

Halawi,

Al-Qahtani

2024

PLoS ONE

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Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many governments ordered citizens to wear masks in public. In this research, we focus on identifying individuals from images or videos by comparing facial features, identifying a person’s biometrics, and reducing the weaknesses of person recognition technology, for example when a person does not look directly at the camera, the lighting is poor, or the person has effectively covered their face. Consequently, we propose a hybrid approach of detecting either a person with or without a mask, a person who covers large parts of their face, and a person based on their gait via deep and machine learning algorithms. The experimental results are excellent compared to the current face and gait detectors. We achieved success of between 97% and 100% in the detection of face and gait based on F1 score, precision, and recall. Compared to the baseline CNN system, our approach achieves extremely high recognition accuracy.

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Section: Plos Onementioning

confidence: 99%

A hybrid human recognition framework using machine learning and deep neural networks

Sheneamer,

Halawi,

Al-Qahtani

2024

PLoS ONE

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“…The suggested system had a 95% accuracy rate. Research by Sadhukhan and Bhattacharya [16] proposed a hybrid face mask detection system. It has used traditional methods, deep learning and handcrafted feature extractors.…”

Section: Literature Surveymentioning

confidence: 99%

Incorrect facemask-wearing detection using image processing and deep learning

Habeeb

2023

Bulletin EEI

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Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

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“…In [ 25 ], the authors introduced a hybrid face mask identification model that combines deep learning, handcrafted feature extractors, and traditional machine learning classifiers. In particular, the proposed approach combines a Random Forest classifier on a hybrid feature set created by CNN and a handcrafted feature extractor from the input pictures to distinguish masks from faces.…”

Section: Machine Learning Prototypes Used To Identify Face Maskmentioning

confidence: 99%

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

Al-Nabulsi

Turab

Owida

et al. 2023

Sensors

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A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.

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HybridFaceMaskNet: A Novel Face-Mask Detection Framework Using Hybrid Approach

Cited by 6 publications

References 12 publications

A hybrid human recognition framework using machine learning and deep neural networks

A hybrid human recognition framework using machine learning and deep neural networks

Incorrect facemask-wearing detection using image processing and deep learning

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

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