HIC-DEEP: A Hierarchical Clustered Deep Learning Model for Face Mask Detection

The use of face masks is apparently not strange in these present days as conceptualized in the past due to the emergence of the Pandemic Covid-19 Corona virus. As part of the non-clinical preventive measures for the spread of this virus is the prescription and proper usage of face mask by the World health organization (WHO). In lieu of this, heads of organizations, directors of industries and individuals have adopted the “No facemask, no entry†policy in varieties of designs placed at their door posts. The state of the arts technologies has also been developed to help detect face mask non-compliant users. Whereas, the use of non-supervised machine learning approach for classifying and detecting Covid-19 facemask compliant users is not widespread. In this paper, HIC-DEEP (an un-supervised machine learning) model is proposed using a pre-trained InceptionV3 network for Kaggle database Image features vector extraction for subsequent computations of Euclidian, Spearman, and Pearson distance matrixes. The Hierarchical clustering method is then activated to identify face mask wearing faces from defiant faces. The distance algorithms all returned a perfect precision rate of 100% for the identification of faces with no face masks while an accuracy of 60%, 78% and 85% are achieved by Spearman, Pearson and Euclidian respectively for the cluster with full face mask compliance. However, the Euclidian distance algorithm returned the best overall accuracy in terms of the distance matrix with data points grouped along close proximities with unique clusters