Face Mask Detection using Transfer Learning of InceptionV3

TL;DR

This paper presents a transfer learning approach using InceptionV3 to accurately detect face mask usage, aiding in COVID-19 prevention efforts with high accuracy.

Contribution

It introduces a fine-tuned InceptionV3 model for face mask detection trained on the SMFD dataset, achieving superior accuracy over existing methods.

Findings

Achieved 99.9% training accuracy

Achieved 100% testing accuracy

Utilized image augmentation for improved training

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.