Open Access

ARTICLE

A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes

Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi^*

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11047, KSA

* Corresponding Author: Norah S. Alghamdi. Email:

Intelligent Automation & Soft Computing 2022, 32(2), 1153-1165. https://doi.org/10.32604/iasc.2022.022359

Received 05 August 2021; Accepted 06 September 2021; Issue published 17 November 2021

Abstract

People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask- wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.

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Keywords

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