Face mask recognition system using MobileNetV2 with optimization function

Figures & data

Figure 1. MobileNetv2 building block.

Figure 2. Preview of face mask dataset.

Figure 3. Phases and individual steps for building a COVID-19 face mask detector with computer vision and deep learning using Python, OpenCV, and TensorFlow/Keras.

Figure 4. Shows the architecture and the number of layers of a pre-trained MobileNetv2 model (Wang et al. 2020).

Figure 5. MobileNetv2 model’s training and validation curves.

Figure 6. Confusion matrix.

Figure 7. Training/Validation accuracy graph of dataset using MobilNetv2.

Table 1. Classification report.

	Precision	Recall	f1-score	Support
dataset/with_mask	1.00	0.98	0.99	389
dataset/without_mask	0.98	1.00	0.99	378
Accuracy			0.99	767
macro avg	0.99	0.99	0.99	767
weighted avg	0.99	0.99	0.99	767

The various metrics used to evaluate the model are accuracy, precision, recall, and f1 score.

Figure 8. Diagram showing implemented face mask model.

Table

REFERENCES		YEAR	ACCURANCY	TECHNOLOY/TOOL USED
S. V Militante and N. V Dionisio,	(Militante and Dionisio 2020)	2020	96%	Raspberry pi B
S. A. Sanjaya and S. A. Rakhmawan	(Sanjaya and Adi Rakhmawan 2020)	2020	96.85%	MobileNetV2 in The Era of COVID-19
P. Gupta, N. Saxena, M. Sharma, J. Tripathi	(Gupta et al. 2018)	2018	97.05%	Haar Cascade DNN
R. Bhuiyan, S. Khushbu, S. Islam	(Bhuiyan, Akter Khushbu, and Sanzidul Islam 2020)	2020	High	YOLO
G. Kaur et al	(Kaur et al. 2021)	2021	High	MobileNetV2/no optimization
Our project		2022	99.21%	MobileNetV2 with optimization

Wang, W., Y. Li, T. Zou, X. Wang, J. You, and Y. Luo. 2020. A novel image classification approach via dense-mobilenet models. Mobile Information Systems 2020:1–8. doi:10.1155/2020/7602384.

 Web of Science ®Google Scholar

Militante, S. V., and N. V. Dionisio. 2020. “Real-time facemask recognition with alarm system using deep learning.” In 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), 106–10. IEEE.

 Google Scholar

Sanjaya, S. A., and S. Adi Rakhmawan. 2020. Face Mask Detection Using MobileNetV2 in The Era of COVID-19 Pandemic. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1–5.

 Google Scholar

Gupta, P., N. Saxena, M. Sharma, and J. Tripathi. 2018. Deep neural network for human face recognition. International Journal of Engineering and Manufacturing (IJEM) 8 (1):63–71. doi:10.5815/ijem.2018.01.06.

 Google Scholar

Bhuiyan, M. R., S. Akter Khushbu, and M. Sanzidul Islam. 2020. “A deep learning based assistive system to classify COVID-19 face mask for human safety with YOLOv3.” In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–5. IEEE.

 Google Scholar

Kaur, G., R. Sinha, P. Kumar Tiwari, S. Kumar Yadav, P. Pandey, R. Raj, A. Vashisth, and M. Rakhra. 2021. Face mask recognition system using CNN model. Neuroscience Informatics 2 (3):100035. doi:10.1016/j.neuri.2021.100035.

 PubMedGoogle Scholar