Comparative Analysis of Image Classification Algorithms for Face Mask Detection

Abstract

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.

Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network

“…According to their experimental results, MobileNet Mask achieved about 93% accuracy with 770 validation samples, and about 100% with 276 validation samples. Naufal et al (2021) conducted a comparative study on face mask detection using support vector machines (SVM), k-nearest neighbors (KNN), and deep CNNs (DCNN). Although CNN required a longer execution time compared to KNN and SVM, it reported the best average performance, with an accuracy of 96,83%.…”

A Review on Deep-Learning-Based Cyberbullying Detection

“…Sometimes exploiting traditional ML models are not reasonable to extract features, whereas in DL-based models, the task is performed automatically in the hidden layers. However, extracting features intelligently is an essential task during cyberbullying detection from text and image [15][16][17][18]. In addition, understanding the context of the text or images increases the chance of providing better accuracy [19][20][21][22][23].…”

Face Mask Wearing Classification Using Machine Learning

“…DTs are a common component of many medical diagnostic regimens, as they are simple to understand and learn. When traversing the tree for the classification of a sample, the outcomes of all tests at each node along the path provides sufficient information to make a conjecture about its class [9].…”