A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Abstract

The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All people should wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the people who have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower the spread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images (HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, a machine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200 HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD.