ABSTRACT
Deep Convolution Neural Network (DCNN) based facial recognition has made significant progress in recent years. Currently, facial recognition technology has emerged as an important authentication tool on mobile devices. Hence, a fast and lightweight DCNN model is required to work accurately in limited computing resources. Meanwhile, the outbreak of the COVID-19 pandemic has led to new challenges in face recognition due to the use of facemasks. Therefore, in this study, we develop a masked face recognition application using a lightweight and efficient DCNN, which is applicable to mobile devices. Two networks for face verification tasks, named MobileFaceNet and SeesawFaceNet are explored for this purpose. We train these models on the augmented version of CelebA dataset, which originally is a set of celebrity images. We put synthetic mask on the face images in CelebA to provide a training dataset contain mix of face images with and without mask. The trained models, which are able to recognize people either wearing or not wearing masks, are then retrained on the face dataset commonly used for verification purposes, i.e. LFW (face images without mask) and MFR2 (face images wearing masks). Transfer learning is utilized to improve the network learning ability, and cosine similarity is adopted to quantify the similarity for pairs of examples. In experiment, the SeesawFaceNet model obtains better performance, with 98.8% accuracy on LFW dataset, 96% accuracy on MFR2 masked dataset. In contrast, the experiment after deployment the models on a smartphone application, the MobileFaceNet model is more superior than the SeesawFaceNet with an accuracy of 85%, an average speed of 44 milliseconds, and model size of 4.9 MB. © 2023 AIP Publishing LLC.
ABSTRACT
Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.