Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities

During the spread of the COVID-I9 pandemic in early 2020, the WHO organization advised all people in the world to wear face-mask to limit the spread of COVID-19. Many facilities required that their employees wear face-mask. For the safety of the facility, it was mandatory to recognize the identity of the individual wearing the mask. Hence, face recognition of the masked individuals was required. In this research, a novel technique is proposed based on a mobile-net and Haar-like algorithm for detecting and recognizing the masked face. Firstly, recognize the authorized person that enters the nuclear facility in case of wearing the masked-face using mobile-net. Secondly, applying Haar-like features to detect the retina of the person to extract the boundary box around the retina compares this with the dataset of the person without the mask for recognition. The results of the proposed modal, which was tested on a dataset from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score 0.98.

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Masked Face Analysis via Multi-Task Deep Learning

Journal of Imaging ◽

10.3390/jimaging7100204 ◽

2021 ◽

Vol 7 (10) ◽

pp. 204

Author(s):

Vatsa S. Patel ◽

Zhongliang Nie ◽

Trung-Nghia Le ◽

Tam V. Nguyen

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Face Recognition ◽

Face Mask ◽

Learning Model ◽

Unified Framework ◽

Improve Performance ◽

Face Analysis ◽

And Gender ◽

Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

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Cross-Age Face Recognition Using Deep Learning Model Based on Dual Attention Mechanism

Lecture Notes in Electrical Engineering - Communications, Signal Processing, and Systems ◽

10.1007/978-981-15-8411-4_251 ◽

2021 ◽

pp. 1911-1919

Author(s):

Jialve Wang ◽

Shenghong Li ◽

Fucai Luo

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Learning Model ◽

Attention Mechanism ◽

Model Based ◽

Deep Learning Model

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Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

Sustainable Cities and Society ◽

10.1016/j.scs.2020.102600 ◽

2020 ◽

pp. 102600

Author(s):

Mohamed Loey ◽

Gunasekaran Manogaran ◽

Mohamed Hamed N. Taha ◽

Nour Eldeen M. Khalifa

Keyword(s):

Deep Learning ◽

Face Mask ◽

Learning Model ◽

Model Based ◽

Deep Learning Model

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Deep Learning Model for Face Mask Based Attendance System in the Era of the Covid-19 Pandemic

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs51430.2021.9441735 ◽

2021 ◽

Author(s):

R. Satheesh Kumar ◽

Anagha Rajendran ◽

V Amrutha ◽

Gopika.T. Raghu

Keyword(s):

Deep Learning ◽

Face Mask ◽

Learning Model ◽

Deep Learning Model

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Deep learning for classification of pediatric chest radiographs by WHO’s standardized methodology

PLoS ONE ◽

10.1371/journal.pone.0253239 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253239

Author(s):

Yiyun Chen ◽

Craig S. Roberts ◽

Wanmei Ou ◽

Tanaz Petigara ◽

Gregory V. Goldmacher ◽

...

Keyword(s):

Deep Learning ◽

World Health Organization ◽

Characteristic Curve ◽

Learning Model ◽

Observer Agreement ◽

World Health ◽

Widespread Application ◽

The World ◽

Health Organization ◽

Deep Learning Model

Background The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of this endpoint. Methods We trained a deep learning model to classify pneumonia CXRs in children using the World Health Organization (WHO)’s standardized methodology. The model was pretrained on CheXpert, a dataset containing 224,316 adult CXRs, and fine-tuned on PERCH, a pediatric dataset containing 4,172 CXRs. The model was then tested on two pediatric CXR datasets released by WHO. We also compared the model’s performance to that of radiologists and pediatricians. Results The average area under the receiver operating characteristic curve (AUC) for primary endpoint pneumonia (PEP) across 10-fold validation of PERCH images was 0.928; average AUC after testing on WHO images was 0.977. The model’s classification performance was better on test images with high inter-observer agreement; however, the model still outperformed human assessments in AUC and precision-recall spaces on low agreement images. Conclusion A deep learning model can classify pneumonia CXR images in children at a performance comparable to human readers. Our method lays a strong foundation for the potential inclusion of computer-aided readings of pediatric CXRs in vaccine trials and epidemiology studies.

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