Open Access

ARTICLE

A Method for Detecting Non-Mask Wearers Based on Regression Analysis

by Dokyung Hwang¹, Hyeonmin Ro¹, Naejoung Kwak², Jinsang Hwang³, Dongju Kim^1,*

1 POSTECH Institute of Artificial Intelligence, Pohang, 24257, Korea
2 Department of Information Security at Pai Chai University, Daejeon, 35345, Korea
3 Department of Safety and Emergency Management at Kyungwoon University, Gumi, 39160, Korea

* Corresponding Author: Dongju Kim. Email:

Computers, Materials & Continua 2022, 72(3), 4411-4431. https://doi.org/10.32604/cmc.2022.025378

Received 22 November 2021; Accepted 21 February 2022; Issue published 21 April 2022

Abstract

A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections. The proposed method quickly and accurately detects mask and facial regions using well-trained You Only Look Once (YOLO) detector, then applies image coordinates of the detected bounding box (bbox). First, the data that is used to train our model is collected under various circumstances such as light disturbances, distances, time variations, and different climate conditions. It also contains various mask types to detect in general and universal application of the model. To detect mask-wearing status, it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images. Furthermore, the Convolutional Neural Network (CNN) model is trained with both our own dataset and open dataset to detect under heavy foot-traffic (Indoors). To make the model robust and reliable in various environment and situations, we collected various sample data in different distances. And through the experiment, we found out that there is a particular gradient according to the mask-wearing status. The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum. Then it carry out the classification of mask-wearing status of detected object. Lastly, we defined and classified three different mask-wearing states according to the mask’s position (With mask, Wear a mask around chin and Without mask). The gradient according to the mask-wearing status, is analyzed through linear regression. The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System. Through the experiments, we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments. And the proposed method, through linear regression analysis, classifies in a very concise way than the others.

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Keywords

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