Face Mask Detection in Real‐Time Video Stream Using Deep Learning

Negi, Atul; Kumar, Krishan

doi:10.1002/9781119818717.ch14

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…In [33][34] plant disease was simulated by AI. Face mask from video streaming was performed in [35].A human action recognition system was designed by RESNET50 that can determine the human daily activities [36]. In [37] liver disease detection was proposed to reduce the instigation of death.…”

Section: Related Workmentioning

confidence: 99%

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

Haque

Hoque²,

Paul

2023

Preprint

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The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

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Section: Related Workmentioning

confidence: 99%

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

Haque

Hoque²,

Paul

2023

Preprint

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“…The body of research in the literature is on the production of masked face image datasets and algorithms for detecting whether a subject is wearing or not wearing a mask. In [35], the authors propose an advanced deep learning model for face mask detection in real-time video streams. In this regard, Negi et al [36] employ two well-known deep neural network architectures with transfer learning for face mask detection using the Simulated Masked Face Recognition Dataset.…”

Section: Related Workmentioning

confidence: 99%

M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis

et al. 2022

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Recent regulations to block the widespread transmission of COVID-19 disease among people impose the use of facial masks indoor and outdoor. Such restriction becomes critical in all those scenarios where access controls take benefit from biometric recognition systems. The occlusions due to the presence of a facial mask make a significant portion of human faces unavailable for feature extraction and analysis. This work explores the contribution of the solely periocular region of the face to achieve a robust recognition approach suitable for mobile devices. Rather than working on a static analysis of the facial features, like largely done by work on periocular recognition in the literature, the proposed study focuses the attention on the analysis of face dynamics so that the spatio-temporal features make the recogniser frameindependent and tolerant to user movements during the acquisition. To obtain a lightweight processing, which is compliant with limited computing power of mobile devices, the spatio-temporal representation of the periocular region has analysed and classified through Machine Learning approaches. The experimental discussion has been performed on a new dataset, Mobile Masked Face REcognition Database, specifically designed to analyse the periocular region dynamics in presence of facial masks. For a wider comparative analysis, a publicly available dataset called XM2VTS has been considered as well as Deep Learning solutions have been experimented to discuss the challenging aspects of the recognition problem. Moreover, a summary of the state-of-the-art on periocular recognition driven by COVID pandemic has been presented, showing how the research efforts in this field focused on recognition of still images. Experimental results show promising levels of performance as well as limitations of the proposed approach, creating the premises for future directions.

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“…Artificial neural networks have been applied in many disciplines for cancer detection and recently to detect and prevent COVID-19 disease [11,38,41]. Deep convolutional neural networks from artificial neural networks have been proven successful in image processing applications [3].…”

Section: Introductionmentioning

confidence: 99%

MediNet: transfer learning approach with MediNet medical visual database

Reis

Turk

Khoshelham

et al. 2023

Multimed Tools Appl

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The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract

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Face Mask Detection in Real‐Time Video Stream Using Deep Learning

Cited by 16 publications

References 10 publications

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis

MediNet: transfer learning approach with MediNet medical visual database

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