Face Mask Detection Using GoogLeNet CNN-Based SVM Classifiers

Year 2023, Volume: 36 Issue: 2, 645 - 658, 01.06.2023

Kubilay Muhammed Sünnetci Selahaddin Batuhan Akben Mevlüde Merve Kara Ahmet Alkan

https://doi.org/10.35378/gujs.1009359

Cited By: 1

Abstract

The COVID-19 pandemic that broke out in 2019 has affected the whole world, and in late 2021 the number of cases is still increasing rapidly. In addition, due to this pandemic, all people must follow the mask and cleaning rules. Herein, it is now mandatory to wear a mask in places where millions of people working in many workplaces work. Hence, artificial intelligence-based systems that can detect face masks are becoming very popular today. In this study, a system that can automatically detect whether people are masked or not is proposed. Here, we extract image features from each image using the GoogLeNet architecture. With the help of these image features, we train GoogLeNet based Linear Support Vector Machine (SVM), Quadratic SVM, and Coarse Gaussian SVM classifiers. The results show that the accuracy (%), sensitivity (%), specificity (%) precision (%), F1 score (%), and Matthews Correlation Coefficient (MCC) values of GoogLeNet based Linear SVM is equal to 99.55-99.55-99.55-99.55-99.55-0.9909. When the results of the proposed system are examined, it is seen that it provides an advantage due to its high accuracy. In addition, it is very useful in practice that it can detect masks from any camera. Moreover, since there are classification models that can be created in a shorter time than models that can detect objects, model results can be examined in a shorter time. Therefore, it is seen that the proposed system also provides an advantage in terms of complexity.

Keywords

Face mask detection, Deep learning, Machine learning, GoogLeNet, SVM

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