Real-time Face Mask Detection Using Machine Learning/ Deep Feature-Based Classifiers For Face Mask Recognition

In this research, we put forward an architecture that combines recent deep learning algorithms with geometry techniques to create robust models that can handle aspects like detection, tracking and validation. This dissertation focuses on creating a model that efficiently utilizes a mix of conventional machine learning and deep learning techniques to categorize the facemasks effectively. Our model comprises two aspects in this hour of need: For dimensionality reduction through feature extraction, the initial element is produced with InceptionV3. The facemask classification procedure is developed with the Logistic Regression (LR) method. Deep learning (DL) is used to efficiently train an architecture using a dataset of photos of people's faces with and without and partial face masks to extract features. The retrieved traits are now passed into various classification algorithms namely Random Forest, Logistic Regression, CNN, Support Vector Machine, AdaBoost and K-Nearest Neighbors to appropriately classify the masks position. Hence, we can project that employing Transfer Learning (TL) and Deep Learning together can detect a properly or improperly worn face mask with high accuracy. This system design stops transmitting this fatal virus by detecting individuals in urban areas who are not wearing facemasks effectively.