Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic

Asif, Sohaib; Yi, Wenhui; Tao, Yi; Si, Jinhai; Amjad, Kamran

doi:10.1109/icaibd51990.2021.9459008

Cited by 24 publications

(10 citation statements)

References 12 publications

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“…To examine the classification performance metrics of the classifiers used in the study, we give Table 3 as follows: When the proposed system and the studies in the literature are compared, it is seen that the classification success of the proposed method is sufficient. Generally, 90% or more accurate is obtained in studies found in the literature [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Due to the low number of images in the dataset used in the proposed system, we do not use deep learning architectures to directly create the models.…”

Section: Discussionmentioning

confidence: 99%

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Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Sünnetci

Akben

KARA

et al. 2023

Gazi University Journal of Science

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The COVID-19 pandemic that broke out in 2019 has affected the whole world, and in late 2021 the number of cases is still increasing rapidly. In addition, due to this pandemic, all people must follow the mask and cleaning rules. Herein, it is now mandatory to wear a mask in places where millions of people working in many workplaces work. For this reason, artificial intelligence-based systems that can detect face masks are becoming very popular today. In this study, a system that can automatically detect whether people are masked or not is proposed. In the dataset used in the study, there are a total of 440 images, 220 with masks and 220 without masks. Here, we extract image features from each image using the GoogLeNet deep learning architecture. Using the loss3-classifier layer of the GoogLeNet deep learning architecture, we obtain 1000 deep image features for each image. With the help of these image features, we train GoogLeNet based Linear Support Vector Machine (SVM), Quadratic SVM, and Coarse Gaussian SVM classifiers. Afterward, we develop a Graphical User Interface (GUI) application of the proposed system and a system that can detect masks from snapshots by saving the trained models. Thus, an application that is based on artificial intelligence and can automatically detect the mask has been developed. From the results, it appears that the accuracy (%), sensitivity (%), specificity (%) precision (%), F1 score (%), and Matthews Correlation Coefficient (MCC) values of GoogLeNet based Linear SVM, Quadratic SVM, and Coarse Gaussian SVM classifiers are equal to 99.55-99.55-99.55-99.55-99.55-0.9909, 99.55-99.55-99.55-99.55-99.55-0.9909, and 99.55-99.09-100-100-99.54-0.9910, respectively. When the results are examined, it is seen that not all classifiers are superior to each other in terms of accuracy. However, while Linear SVM and Quadratic SVM classifiers are more successful in terms of sensitivity and F1 score, it is seen that the Coarse Gaussian SVM classifier is better in terms of specificity, precision, and MCC.

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Section: Discussionmentioning

confidence: 99%

“…After detecting the face, MobileNetv2 architecture has been used to determine the mask area. According to the results, the training and validation accuracy values of the proposed model are equal to 99.2% and 99.8%, respectively [24]. A hybrid model has been developed by using deep learning and machine learning in this study.…”

Section: Introductionmentioning

confidence: 95%

Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Sünnetci

Akben

KARA

et al. 2023

Gazi University Journal of Science

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“…The comparison is conducted using a train test ratio of 80:20. The comparison of the proposed models with the finding of Asif et al [9] and Sadeddin [10] on Dataset 2 is presented in Table 9. The comparison is conducted using a train test ratio of 80:20.…”

Section: Comparison With Others Research Findingmentioning

confidence: 99%

“…Asif et al [9] applied OpenCV and ML to recognize and track faces. Then, MobileNetV2 was employed to determine the mask region from the processed face frames.…”

Section: Related Workmentioning

confidence: 99%

Face Mask Detection Using Deep Learning

Azian¹,

Ng²,

Yap³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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It is vital to remain vigilant during pandemic COVID-19. Wearing a face mask is one of the crucial steps that people must take to ensure that they are a step away from spreading and infecting the virus. However, controlling and monitoring people in a densely crowded place is tough. Hence, a face mask detection system in public area is needed to remotely monitor if one is wearing a face mask or vice versa. In this study, two face masks datasets are downloaded from GitHub with 3834 images and 11800 colour images. Data pre-processing steps are carried out before the classification, which includes image resizing, converting images into array and label encoding. Two deep learning models, MobileNetV2 and VGG19, are developed for detection and evaluation. The experimental results performed by MobileNetV2 outperformed the VGG19 with achieving accuracy of 98.96% and 99.55% on Dataset 1 and Dataset 2 respectively.

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“…It is linked to a closed-circuit television (CCTV) mechanism verifying that only individuals wearing masks are allowable. Asif et al [14] propose automatically utilizing DL to identify face masks in the video. The presented structure has 2 elements.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

AL-Ghamdi¹,

Alshammari²,

Ragab³

2023

Computer Systems Science and Engineering

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Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

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Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic

Cited by 24 publications

References 12 publications

Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Face Mask Detection Using Deep Learning

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

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