Face Mask Detection using Transfer Learning of InceptionV3

Abstract

The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). Several guidelines were issued by the World Health Organization (WHO) for protection against the spread of coronavirus. According to WHO, the most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask. The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3. The proposed model is trained and tested on the Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted to address the limited availability of data for better training and testing of the model. The model outperformed the other recently proposed approaches by achieving an accuracy of 99.9% during training and 100% during testing.

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Cited by in corpus (4)

• Modality specific U-Net variants for biomedical image segmentation: A survey
• A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints
• WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19
• Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey