Detecting Masked Faces using Region-based Convolutional Neural Network

Gathani, Jenil; Shah, Krish

doi:10.1109/iciis51140.2020.9342737

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…However, the models were trained with 680 and 1400 images respectively. R-CNN model to detect face mask [34] . The R-CNN model uses TensorFlow Object Detection API to detect non-masked and masked faces.…”

Section: Resultsmentioning

confidence: 99%

“…The second one is the refinement stage where the image region that is within each proposal is warped to a fixed size, it is then mapped to a 4096-dimensional feature vector which is fed into a classifier and also into a regressor that refines the position of the detection [78] . For instance, in the context of COVID-19, Gathani [34] applied R-CNN to classify masked faces and non-masked faces in a learning-based system. The model achieved a precision detection accuracy of 68.72% and a mean average precision of 85.82%.…”

Section: Resultsmentioning

confidence: 99%

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Application of deep learning and machine learning models to detect COVID-19 face masks - A review

Mbunge

Simelane

Fashoto

et al. 2021

Sustainable Operations and Computers

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The continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks especially in public places where massive contact of people is frequent and inevitable, particularly inside public transport facilities, sports arenas, shopping malls and workplaces. However, compliance and adherence to proper wearing of face masks have been difficult due to various reasons including diversified mask types, different degrees of obstructions, various variations, balancing various model detection accuracy or errors and deployment requirements, angle of view, deployment of detection model on computers with limited processing power, low-resolution images, facial expression, and lack of real-world dataset. Therefore, this study aimed at providing a comprehensive review of artificial intelligence models that have been used to detect face masks. The study revealed that deep learning models such as the Inceptionv3 convolutional neural network achieved 99.9% accuracy in detecting COVID-19 face masks. We deducted that most of the datasets used to detect face masks are created artificially, do not represent the real-world environments which ultimately affect the precision accuracy of the model when deployed in the real world. Hence there is a need for sharing real-world COVID-19 face mask images for modelling deep learning techniques. The study also revealed that deeper and wider deep learning architectures with increased training parameters, such as inception-v4, Mask R-CNN, Faster R-CNN, YOLOv3, Xception, and DenseNet are not yet implemented to detect face masks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of deep learning and machine learning models to detect COVID-19 face masks - A review

Mbunge

Simelane

Fashoto

et al. 2021

Sustainable Operations and Computers

View full text Add to dashboard Cite

show abstract

“…It is observed that the above methods are the prevailing methods used in the detection of facemask and publicly available dataset online have been predominantly used [6,24,39,41] and [31]. Among these methods, the deep learning algorithms has significantly outperformed other techniques used, and the convolutional neural network has even perform better than the other deep learning algorithms like YOLO, MobileNet, ResNet etc [6,24]. The convolutional neural network model has constantly obtain accuracy values that ranges from 95% to 99.99%.…”

Section: Discussionmentioning

confidence: 99%

Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Abiodun

Ogbuju

Oladipo

2022

J. Appl. Artif. Intell.

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After the emergence of the Coronavirus disease (Covid19) in December, 2019 the virus was confirmed by the World Health Organization (WHO) to be a dangerous virus that spread through airborne and droplets. One of the most effective way of preventing the spread of this virus is the use of facemask in public places such as banks, shopping mall, schools, offices and the likes. For this reasons people are advised to use facemask whenever they go out for their daily activities. However, some people have refused to wear the facemask in public places, thereby increasing the rate at which the virus spreads. Hence, there is a need to design an access control system for use in public places to grant access to people complying with the facemask regulations or deny access to those faulting them. Literature has shown that the use of computer vision and deep learning technology can play important role in the deployment of such access control system for face detection and facemask detection. The computer vision and deep learning techniques used in face recognition and facemask detection are describes in this systematic literature review.

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“…Gathani and Shah [12] used TensorFlow Object Detection-based R-CNN model to detect non-masked and masked faces. The method achieved 68.72% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Face Mask Recognition System using Adapted Capsule Neural Networks for Medical Institutions

El-Magd

Goda

Darwish

et al. 2023

Preprint

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Masks are essential, especially in medical institutions, due to the global spread of illnesses and epidemics. This paper presents an unprecedented neural network called the capsule network for face mask recognition. The capsule network has proven to be most suitable for real-life image recognition, as it relies on the spatial relationship features of the image. This paper presents an adapted capsule network by adding a block for deep feature extraction. The proposed system has two phases; the first phase usesVGG16 and VGG19 as a pre-training module for the feature extractions, while the second phase is based on the Capsule network for the face mask recognition phase. Two benchmark datasets are used to test the proposed approach; Real-World Masked Face Dataset (RMFD) and Simulated Masked Face Recognition Dataset (SMFRD).The accuracy of the testing system based on RMFD data sets of CapsNet, VGG16, and VGG19 is 99.87%, 99.90%, and 99.94%, respectively. In contrast, the accuracy of CapsNet with VGG19 reaches 99.94% on the SMFD data. Comprehensive experiments demonstrate the effectiveness of the presented face mask recognition system.

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Detecting Masked Faces using Region-based Convolutional Neural Network

Cited by 10 publications

References 14 publications

Application of deep learning and machine learning models to detect COVID-19 face masks - A review

Application of deep learning and machine learning models to detect COVID-19 face masks - A review

Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

Face Mask Recognition System using Adapted Capsule Neural Networks for Medical Institutions

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