Using Convolutional Neural Network for Detection of Face Mask

Research Article
Open access

Using Convolutional Neural Network for Detection of Face Mask

Jiajin Yang 1*
  • 1 Xi’an Jiaotong-Liverpool University    
  • *corresponding author Jiajin.Yang20@student.xjtlu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230295
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

The COVID-19 pandemic has had a sweeping impact across the globe, resulting in enormous economic losses and significant changes to people's way of life. Despite the World Health Organization's (WHO) assertion that the COVID-19 pandemic will conclude by 2023 and people's lives will begin to settle down, the possibility of a resurgence of the virus cannot be overlooked. In crowded public places, it is essential to have a system that can rapidly and accurately detect whether people are wearing masks and adhering to proper usage protocols. Therefore, it is crucial to make necessary preparations for such a system. Fortunately, there have been notable advancements in facial recognition technology, which can aid in this endeavor. This paper aims to build a model for face-mask-detection with convolutional neural network to help perform a rapid mask-wearing check and carry out the system model training with a final accuracy reaching 0.95. In the end, high accuracy is observed in the classification of correctly wearing masks and incorrectly wearing masks, demonstrating that the model is capable to identify whether people wear masks and wear correctly with the final accuracy reaching 0.97.

Keywords:

facial recognition, face mask detection, convolutional neural network

Yang,J. (2023). Using Convolutional Neural Network for Detection of Face Mask. Applied and Computational Engineering,8,667-677.
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References

[1]. Tang Renwu,Li Chuqiao & Ye Tianxi. (2020). The damage of the new coronavirus pneumonia epidemic to China's economic development and countermeasures. Economics and Management Research(05),3-13.] doi:10.13502/j.cnki.issn1000-7636.2020.05.001.

[2]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.), 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

[3]. Trigueros, D. S., Meng, L., & Hartnett, M. (2018). Face recognition: From traditional to deep learning methods. arXiv preprint arXiv:1811.00116.

[4]. Face Mask Detection. URL: https://www.kaggle.com/datasets/andrewmvd/face-mask-detection, 2022.

[5]. X.Zhang and X.Wang. (2016). Novel Survey on the Color-Image Graying Algorithm. 2016 IEEE International Conf. on Computer and Information Technology (CIT), pp. 750-753.

[6]. Alnowami Majdi et al. (2022). MR image normalization dilemma and the accuracy of brain tumor classification model. Journal of Radiation Research and Applied Sciences, 15(3), pp. 33-39.

[7]. S.Yadav and S.Shukla. (2016). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. IEEE 6th International Conf. on Advanced Computing (IACC), pp. 78-83.

[8]. S. I. Yudita, T. Mantoro and M. A. Ayu. (2021). Deep Face Recognition for Imperfect Human Face Images on Social Media using the CNN Method. 2021 4th International Conf. of Computer and Informatics Engineering (IC2IE), pp. 412-417.

[9]. Deng Jianguo, Zhang Sulan, Zhang Jifu, Xun Yaling & Liu Aiqin.(2020). Loss function and its application in supervised learning. Big Data (01),60-80.

[10]. G. Saranya, D. Sarkar, S. Ghosh, L. Basu, K. Kumaran and N. Ananthi. (2021).Face Mask Detection using CNN. 2021 10th IEEE International Conf. on Communication Systems and Network Technologies (CSNT), pp. 426-431.

[11]. Parekh Disha and Dahiya Vishal. (2021). Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score. Biomedical and Biotechnology Research Journal (BBRJ), 5(3), pp. 331-334.

[12]. Valero-Carreras Daniel and Alcaraz Javier and Landete Mercedes. (2023). Comparing two SVM models through different metrics based on the confusion matrix. Computers and Operations Research, 152.

[13]. Almonacid, C., Fitas, E., Sánchez-Covisa, J., Gutiérrez, H., & Rebollo, P. (2023). Geographical differences in the use of oral corticosteroids in patients with severe asthma in Spain: heat map based on existing databases analyses. BMC pulmonary medicine, 23(1), 3. https://doi.org/10.1186/s12890-022-02295-2

Cite this article

Yang,J. (2023). Using Convolutional Neural Network for Detection of Face Mask. Applied and Computational Engineering,8,667-677.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Tang Renwu,Li Chuqiao & Ye Tianxi. (2020). The damage of the new coronavirus pneumonia epidemic to China's economic development and countermeasures. Economics and Management Research(05),3-13.] doi:10.13502/j.cnki.issn1000-7636.2020.05.001.

[2]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.), 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

[3]. Trigueros, D. S., Meng, L., & Hartnett, M. (2018). Face recognition: From traditional to deep learning methods. arXiv preprint arXiv:1811.00116.

[4]. Face Mask Detection. URL: https://www.kaggle.com/datasets/andrewmvd/face-mask-detection, 2022.

[5]. X.Zhang and X.Wang. (2016). Novel Survey on the Color-Image Graying Algorithm. 2016 IEEE International Conf. on Computer and Information Technology (CIT), pp. 750-753.

[6]. Alnowami Majdi et al. (2022). MR image normalization dilemma and the accuracy of brain tumor classification model. Journal of Radiation Research and Applied Sciences, 15(3), pp. 33-39.

[7]. S.Yadav and S.Shukla. (2016). Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. IEEE 6th International Conf. on Advanced Computing (IACC), pp. 78-83.

[8]. S. I. Yudita, T. Mantoro and M. A. Ayu. (2021). Deep Face Recognition for Imperfect Human Face Images on Social Media using the CNN Method. 2021 4th International Conf. of Computer and Informatics Engineering (IC2IE), pp. 412-417.

[9]. Deng Jianguo, Zhang Sulan, Zhang Jifu, Xun Yaling & Liu Aiqin.(2020). Loss function and its application in supervised learning. Big Data (01),60-80.

[10]. G. Saranya, D. Sarkar, S. Ghosh, L. Basu, K. Kumaran and N. Ananthi. (2021).Face Mask Detection using CNN. 2021 10th IEEE International Conf. on Communication Systems and Network Technologies (CSNT), pp. 426-431.

[11]. Parekh Disha and Dahiya Vishal. (2021). Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score. Biomedical and Biotechnology Research Journal (BBRJ), 5(3), pp. 331-334.

[12]. Valero-Carreras Daniel and Alcaraz Javier and Landete Mercedes. (2023). Comparing two SVM models through different metrics based on the confusion matrix. Computers and Operations Research, 152.

[13]. Almonacid, C., Fitas, E., Sánchez-Covisa, J., Gutiérrez, H., & Rebollo, P. (2023). Geographical differences in the use of oral corticosteroids in patients with severe asthma in Spain: heat map based on existing databases analyses. BMC pulmonary medicine, 23(1), 3. https://doi.org/10.1186/s12890-022-02295-2

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