An Ensemble Learning Based Approach for Real-Time Face Mask Detection

Manjunatha Guru V.G.¹ , Kamalesh V.N.² , Apoorva K.B.³

1. Department of Computer Science, GFGC, Honnali -577217, India.
2. Gandhinagar University, Gandhinagar, Gujarat – 382721, India.
3. Former Lecturer, GEC, Karnataka, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.3 , pp.8-13, Jun-2024

Online published on Jun 30, 2024

Copyright © Manjunatha Guru V.G., Kamalesh V.N., Apoorva K.B. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Face mask detection system is recently gained a wider interest from the computer vision research community after the COVID-19 outbreak. As highlighted in the literature, so far a considerably small amount of research is conducted to detect mask over face. Hence, our research contribution aims to a build a technique that can accurately detect mask over the face in public areas to restrict the spread of pandemic diseases. In this paper, the ensemble learning based technique is incorporated in order to effectively address the face mask detection problem. The combination of HOG (i.e. Histogram of Oriented Gradients) and Random Forest is newly explored in the face mask detection literature. Further, the experiments are conducted on our own dataset which is created by using freely available Google images. Also, the proposed approach is tested on RMFD dataset (i.e. Real-World Masked Face Dataset) for the effective comparative analysis. The experimental results have shown that our proposed approach is promising and effective in the face mask detection literature.

Key-Words / Index Term :
Bagging, COVID-19, Ensemble, Face, HOG, Mask detection, Random Forest

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