Face detection based on K-medoids clustering and associated with convolutional neural networks.

Over the last several years, the COVID-19 epidemic has spread over the globe. People have become used to the novel standard, which involves working from home, chatting online, and keeping oneself clean, to stop the spread of COVID-19. Due to this, many public spaces make an effort to make sure that their visitors wear proper face masks and maintain a safe distance from one another. It is impossible for monitoring workers to ensure that everyone is wearing a face mask; automated solutions are a far better option for face mask identification and monitoring to assist control public conduct and reduce the COVID-19 epidemic. The motivation for developing this technology was the need to identify those individuals who uncover their faces. Most of the previously published research publications focused on various methodologies. This study built new methods namely K-medoids, K-means, and Fuzzy K-Means(FKM) to use image pre-processing to get the better quality of the face and reduce the noise data. In addition, this study investigates various machine learning models Convolutional neural networks (CNN) with pre-trained (DenseNet201, VGG-16, and VGG-19) models, and Support Vector Machine (SVM) for the detection of face masks. The experimental results of the proposed method K-medoids with pre-trained model DenseNet201 achieved the 97.7 % accuracy best results for face mask identification. Our research results indicate that the segmentation of images may improve the identification of accuracy. More importantly, the face mask identification tool is more beneficial when it can identify the face mask in a side-on approach.