Utilizing Deep Learning for Face Mask Detection

Abstract

The COVID-19 epidemic has led to the extensive implementation of face masks, compelling service providers to require their usage for clients, highlighting the significance of community assistance. A system has been created using advanced technical tools such as TensorFlow, Keras, OpenCV, and Scikit-Learn to properly identify faces that are wearing masks in photos. This is achieved by utilizing convolutional neural networks, which ensure a high level of accuracy. To do this, the model is trained on a dataset that includes both faces with masks and faces without masks. Advanced techniques like edge detection are used to avoid the problem of overfitting. The system achieves noteworthy accuracy rates by performing preprocessing on input images, extracting features using convolution layers, lowering dimensionality, and applying fully connected layers for classification. By refining array models, fine-tuning hyperparameters, and employing data augmentation approaches, the use of arrays for mask detection has become crucial in overseeing the transmission of contagious diseases in public areas, enhancing safety precautions and security protocols worldwide.