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. 2021 Jan 1:167:108288.
doi: 10.1016/j.measurement.2020.108288. Epub 2020 Jul 28.

A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

Mohamed Loey  1 , Gunasekaran Manogaran  2   3 , Mohamed Hamed N Taha  4 , Nour Eldeen M Khalifa  4
Affiliations

A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

Mohamed Loey et al. Measurement (Lond). .

Abstract

The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

Keywords: COVID-19; Classical machine learning; Deep transfer learning; Masked face.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
RMFD dataset images samples.
Fig. 2
Fig. 2
SMFD dataset images samples.
Fig. 3
Fig. 3
LFW dataset images samples.
Fig. 4
Fig. 4
The proposed deep transfer learning model.
Fig. 5
Fig. 5
Proposed ResNet-50 as the feature extractor.
Fig. 6
Fig. 6
Decision trees classifier validation accuracy with performance metrics for the different datasets.
Fig. 7
Fig. 7
Consumed time for the training process for the decision trees classifier for different datasets.
Fig. 8
Fig. 8
Testing accuracy with performance metrics for the decision trees classifier with different testing strategies.
Fig. 9
Fig. 9
SVM classifier validation accuracy with performance metrics for the different datasets.
Fig. 10
Fig. 10
Consumed time for the training process for the SVM classifier for the different datasets.
Fig. 11
Fig. 11
Testing accuracy with performance metrics for the SVM classifier with different testing strategies.
Fig. 12
Fig. 12
Ensemble classifier validation accuracy with performance metrics for the different datasets.
Fig. 13
Fig. 13
Consumed time for the training process for the ensemble classifier for the different datasets.
Fig. 14
Fig. 14
Testing accuracy with performance metrics for the ensemble classifier with different testing strategies.
Fig. 15
Fig. 15
Confusion matrix of testing accuracy for (a) DS1, (b) DS2, and (c) DS3 for the SVM classifier over the training of DS3.
Fig. 16
Fig. 16
Confusion matrix of the testing accuracy for (a) DS1, (b) DS2, and (c) DS3 for the ensemble classifier over the training of DS3.
See this image and copyright information in PMC

References

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