A real time face mask detection system using convolutional neural network

Goyal, Hiten; Sidana, Karanveer; Singh, Charanjeet; Jain, Abhilasha; Jindal, Swati

doi:10.1007/s11042-022-12166-x

Cited by 60 publications

(26 citation statements)

References 21 publications

(18 reference statements)

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“…This method uses the benefits of the 3D facial model and gets a better recognition effect under specific conditions. However, the drawback is that it is highly affected by lighting and the calculation is very large [13] .…”

Section: Related Workmentioning

confidence: 99%

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Kong

Zhou

2023

Computer Communications

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Section: Related Workmentioning

confidence: 99%

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Kong

Zhou

2023

Computer Communications

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“…Some examples of CNN's-based face mask detection include: Mahmoud et al (2021) proposed a system that used hybrid approach to detect face masks in real-time in surveillance videos, achieving an accuracy of 97.51% [9]. Another system proposed by Goyal et al (2022) that used OpenCV and CNN to detect face masks in images, achieving an accuracy of 98.0% [10]. An article by Talahua et al (2021) also proposed an algorithm for Face Mask detection with MobileNetV2 architecture and the OpenCv model that achieve an accuracy of 99.96 %.…”

Section: Iiirelated Workmentioning

confidence: 99%

Segmentation and classification of specific pattern of Brain tumor using CNN

Alshanketi¹

2023

IJETMS

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During the Covid-19 pandemic, it was compulsory to wear a mask. Even in the current situation, when the pandemic is not over. A mask is essential and can be mandated from time to time for specific purposes and conditions. Manually monitoring the masks for large numbers of visitors or attendees for an event or at any public place is challenging and requires much manual work. It is time-consuming, and we cannot completely monitor it. Machine learning based approaches and automated monitoring using security cameras can be a possible solution to minimize the need for large manpower. Many models have been proposed for mask detection using machine learning but still there is a chance for improving the accuracy. Further the training time is high in these cases were normally the train and test ratio for dataset is 80:20 that takes a good chunk of time in training. This study proposes a machine learning-based face detection mechanism using MobileNet_v2 transfer learning techniques to provide very high accuracy in detecting the people not wearing masks or not wearing them properly. Further this study proposes to minimize the training time for speedy and real-time analysis by selecting an optimal combination of train and test dataset ratio. Finally, the model has been implemented using the webcam tool to recognize mask wearers in real time with high accuracy.

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“…Handling an imbalanced dataset is important for better classification [25,26]. According to the papers described above, deep learning architectures are rapidly being applied to facemask detection to prevent COVID-19 spread using a transfer learning-based deep neural network [27][28][29][30][31][32][33][34]. Other deep learning [35][36][37][38][39][40][41][42] and optimization algorithms are also used to solve various optimization problems [43][44][45][46][47][48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

Ali

Shanmugam²,

Muthusamy³

et al. 2022

Electronics

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The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus community transmission. The main contribution of the proposed work is the development of a real-time system for determining whether the person on a webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pretrained models and stringent testing on an objective dataset led to the development of a highly dependable and inexpensive solution. The findings provide validity to the application’s potential for use in real-world settings, contributing to the reduction in pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity, precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and recall. An appropriate balance is kept between the number of necessary parameters and the time needed to conclude the various models.

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A real time face mask detection system using convolutional neural network

Cited by 60 publications

References 21 publications

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Segmentation and classification of specific pattern of Brain tumor using CNN

Real-Time Facemask Detection for Preventing COVID-19 Spread Using Transfer Learning Based Deep Neural Network

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