Project on Machine Learning - Covid19 Mask Detector

To address these challenges, an automated system for face mask detection using deep learning (DL) algorithms has been proposed to control the spreading of this infectious disease effectively. Distinct methods using machine learning and deep learning can be used effectively. In this course, all the essential requirements for such a model have been studied. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources of multiple datasets are mentioned. The several software needed for the implementation is also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research.

Extraction of features is a way to get rid of unnecessary information from the data, thereby reducing the computational cost and still having imperative and relevant data reserved. Also, the reduced data helps increase the model’s learning rate. Moreover, real-time face mask detection leverages machine learning and deep learning techniques for feature extraction. In deep learning, neural networks themselves facilitate extracting features without human intervention.