An Adaptive Neural Network Model for Clinical Face Mask Detection

Abstract

Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This area is now very prominent due to the coronavirus disease pandemic and the post-pandemic phases where wearing of clinical face mask is imminent. Wearing a protective face mask in public and a clinical face mask in a hospital environment slows the spread of the virus and any other respiratory-related contagious diseases, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people’s faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed technique utilized the Faster R-CNN model on the Inception V3 backbone to reduce system complexity and dataset requirements. The model was trained and validated with very few datasets and evaluation results show an overall accuracy of 96% regardless of skin tone.