A transfer learning approach with convolutional neural network for Face Mask Detection

TL;DR

This paper presents a transfer learning-based convolutional neural network system using Inception v3 architecture for accurate face mask detection, including incorrect mask usage, achieving over 99% accuracy.

Contribution

It introduces a novel face mask detection system that classifies masked, unmasked, and incorrectly masked faces using transfer learning and optimized hyper-parameters.

Findings

Achieved 99.47% training accuracy

Achieved 99.33% testing accuracy

Can detect incorrect mask usage

Abstract

Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced an enormous crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detecting facemasks in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including the Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN) This paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked faces, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so, this method has achieved an accuracy of 99.47% and 99.33% in training and test data respectively