Open AccessMachine Learning for Masked Face Recognition in COVID-19 Pandemic Situation
Author(s) -
Mehreen Fatima,
Sajjad A. Ghauri,
Nooh B. Mohammad,
Hannan Adeel,
Mubashar Sarfraz
Publication year - 2022
Publication title -
mathematical modelling and engineering problems/mathematical modelling of engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.090135
Subject(s) - artificial intelligence , forehead , facial recognition system , computer science , support vector machine , three dimensional face recognition , pattern recognition (psychology) , classifier (uml) , face (sociological concept) , covid-19 , computer vision , face detection , random forest , speech recognition , infectious disease (medical specialty) , medicine , disease , social science , surgery , pathology , sociology
In the current epidemic, the whole world is suffering with the infectious disease i.e., Corona Virus Disease (COVID-19). It is important to wear a mask to minimize the transmission of the disease. When everyone is wearing a face mask, it is difficult for recognition systems to recognize the masked face of a specific person. As some of the facial features are covered behind the mask e.g., mouth and nose. Therefore, the face-recognizing systems are inefficient to recognize the masked faces. To solve this issue, a face recognition system is proposed to recognize masked and unmasked faces. Support vector machine (SVM) and Random Forest (RF) based classifiers are trained on the specific dataset and classifiers effectively recognize the masked and unmasked faces. The classifier recognizes the human facial features such as eyes, eyebrows, forehead, ears, and hair. The dataset is collected in the form of images for 28 classes with and without a face mask. The trained system will recognize the person, whether the person is wearing a mask or not. The recognition accuracy is approximately 98.2% for different classes and the proposed recognizer is also compared with the state of art existing techniques.