A Deep Learning-based Approach for Real-time Facemask Detection

Boulila, ‪Wadii; Alzahem, Ayyub; Almoudi, Aseel; Afifi, Muhanad; Alturki, Ibrahim; Driss, Maha

doi:10.1109/icmla52953.2021.00238

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…Furthermore, Teboulbi et al [ 24 ] developed a deep-learning based model for face mask detection and social distancing measurement by utilizing different CNN-based architectures. In short, several articles presented in the recent literature for face mask detection are based on CNN architectures [ 25 , 26 , 27 ]. In these articles, the authors compared the performance of two or three CNN-based architectures and proposed a model which achieved comparatively high accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Habib

Alsanea²,

Aloraini

et al. 2022

Sensors

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Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.

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Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Habib

Alsanea²,

Aloraini

et al. 2022

Sensors

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“…Object detection plays a key role in the field of Artificial Intelligence (AI) using Deep Learning (DL) and Computer Vision (CV) applications such as face detection, people or object counting, vehicle detection, and object classification. During the current pandemic situation, CV contributes to managing the COVID-19 crisis through face mask detection, social distance measuring, diagnostics, and disease classification applications [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, the researchers reported the usage of different ML models. It is observed that Convolutional Neural Network (CNN) is most widely used in face mask detection or face recognition the researchers [1][2][3][4][5]. Most of the literature addresses face mask detection or face recognition independently.…”

Section: Introductionmentioning

confidence: 99%

“…A real-time face mask detection system is implemented using Nvidia Jetson Xavier NX [2]. CNN model based on MobileNetV2 for real-time face mask detection is implemented on a PC with NVIDIA GetForce GTX1650 [3]. Another research done by [4], to study and implementation of three different algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and MobileNet, to find out the most suitable one for face mask detection and reported MobileNet outperforms the other two methods considered.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of facial mask detection and verification of vaccination certificate using Jetson Xavier kit

Dhandapani¹,

Al-Ghafri²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Computer Vision (CV) based object detection system is used for locating and recognizing objects of interest in the digital images or videos/live streams. In this work, real-time face mask detection and verification of vaccination certificates are implemented using a Jetson Xavier kit. Initially, live stream video is captured through a Universal Serial Bus (USB) High Definition (HD) video camera. Then, pre-processed using the standard Python CV library to grab a frame for further processing. Using Haar-Cascade Classifier and face encoding, recognition of the face from the frame is performed and labeled appropriately, then a pre-trained Deep Learning (DL) model is used to locate the target class (mask) on the frame. The Quick Response (QR) code embedded in the vaccination certificate is also analyzed from the frame. The beneficiary and vaccination details are obtained from the website of the concerned authority. Finally, the unique QR code with the relevant information is stored in the Jetson Xavier kit for detection and retrieval during the next instance. The system’s performance is verified and found to be good in terms of accuracy compared to other methods considered in this work. The implementation of this work will cater to the local community and help achieve the nation’s sustainable development goal.

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“…Even if only used for scrutiny purposes, the system must be able to identify the human face and the mask. With the fusion of deep learning and computer vision, another system proposed for the detection of facemasks was presented in[21]. The proposed system identifies faces of individuals wearing masks from video/image frames using image processing, OpenCV, and deep learning frameworks like TensorFlow, Keras, and PyTorch.…”

mentioning

confidence: 99%

IoT-Deep Learning Based Face Mask Detection System for Entrance and Exit Door

Bekele

Gutema

Tujuba

et al. 2022

IJEER

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During the pandemic, it has been seen that the global population follows the guidelines issued by the health organization regarding wearing face masks, but some people do not take care of this and do not use masks. The objective of the proposed system, Wollega University Face Mask Detection System (WUFMDS), is to restrict people who are not wearing a mask on the door side by identifying the face mask from the face or open the door if the incoming person is wearing the mask. This system is based on the Internet of Things (IoT) and a Deep Learning algorithm called Convolutional Neural Network (CNN). For this purpose, images with and without masks were collected as samples from the university. The CNN algorithm is used to detect the mask and classify it as with or without masks. The IoT module controls the door operation based on the classification response sent to the IoT module by the CNN algorithm. The system was tested lively with the dummy door system in order to ensure the functionality of the face mask detection system and developed software applications for the system model are working as defined objectives. Our model had 99.36% accuracy with the training dataset and 99.29% accuracy with the validation set. Hence, the proposed system could be used for the automatic identification and classification of masks on the face and to operate the door to allow the person who is wearing the mask to pass through while keeping it closed when no mask is found on the face.

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A Deep Learning-based Approach for Real-time Facemask Detection

Cited by 16 publications

References 14 publications

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Implementation of facial mask detection and verification of vaccination certificate using Jetson Xavier kit

IoT-Deep Learning Based Face Mask Detection System for Entrance and Exit Door

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