Face Mask Detection System using CNN

Sakshi, Sneha; Gupta, Ajay Kumar; Yadav, Sudeept Singh; Kumar, Uttam

doi:10.1109/icacite51222.2021.9404731

Cited by 24 publications

(7 citation statements)

References 8 publications

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“…It is also small, fast, and excellent for real-time detection and mobile hardware deployment. Sakshi et al [9] created a face mask detector based on MobileNetV2 architecture utilizing Keras/TensorFlow. The model was changed to guarantee face mask recognition in real-time video or still pictures.…”

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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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 trained model was evaluated on both real-time videos and static images, and it performed better in both scenarios than the other planned model. The suggested model on face mask detection was completed using the model constructed with CNN architecture utilizing MobileNetV2, which yielded an excellent result with flawless detection accuracy [23].…”

Section: E Mobilenetv2mentioning

confidence: 99%

Enhanced Real-Time Detection of Face Mask With Alarm System Using Mobilenetv2

Sueno¹,

Amar²,

Menasalvas³

et al. 2022

EPH IJSE

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To prevent the coronavirus from spreading, the government adopted measures such as wearing a face mask in public locations. The researchers aimed to create a face detection system using the MobilenetV2 architecture that would identify a person’s faces and determine whether they were wearing a face mask. The built model will help to reduce the danger of viral transmission. In this study, face mask detection is achieved using a machine learning algorithm and the classification method using MobileNetV2. The steps for building the model are data gathering, data pre-processing, splitting the data, testing the model, and implementing the model. The built model can distinguish between those who are wearing a face mask (with no design patterns) and those who are not wearing it with a 96% accuracy. In terms of classification accuracy, the proposed model using MobileNetV2 outperformed the other models LeNet-5, AlexNet, and ResNet-50. If the detected person is labeled with “no mask”, the system generates an alarm sound. This research will be useful in combating virus spread and avoiding virus contact.

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“…COVID-19 has made us understand the need to know the severe consequences of not wearing one now more than ever. However, it is crucial to implement face mask detectors at bus stations, crowded residential areas, market places, educational institutions, and treatment centers to ensure public safety [8].…”

Section: Introductionmentioning

confidence: 99%

Face mask detection using deep learning on NVIDIA Jetson Nano

Hassan

Abed²,

Abdalla³

2022

IJECE

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<span>In December 2019, the coronavirus pandemic started. Coronavirus desease-19 (COVID-19) is transmitted directly from contaminated surfaces via direct touch. To combat the virus, a multitude of equipment is needed. Masks are a vital element of personal protection in crowded places. As a result, determining if a person is wearing a face mask is critical to assimilating to contemporary society. To accomplish the objective, the model presented in this paper used deep learning libraries and OpenCV. This approach was chosen for safety concerns due to its high resource efficiency during deployment. The classifier was built using the MobileNetV2 structure, which was designed to be lightweight and capable of being utilized in embedded devices such as the NVIDIA Jetson Nano to do real-time mask recognition. The stages of model construction were collecting, pre-processing, splitting data, creating the model, training the model, and applying the model. This system utilized image processing techniques and deep learning to process a live video feed. When someone is not wearing a mask, the output eventually produces an alarm sound through a built-in buzzer. Experimental results and testing were used to verify the suggested system's performance. Including both training and testing, the achieved recognition rate was 99%.</span>

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Face Mask Detection System using CNN

Cited by 24 publications

References 8 publications

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

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

Enhanced Real-Time Detection of Face Mask With Alarm System Using Mobilenetv2

Face mask detection using deep learning on NVIDIA Jetson Nano

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