Masked Face Recognition Using Convolutional Neural Networks

Since the COVID-19 epidemic's rise in 2020, Cover face recognize achieve advanced significantly range of computer vision. Face cover is important to stop or limit disease's spread due global outbreak. among most commonly used biometric recognition approach, because it can beutilized for monitoring systems, identity management, security verifying, and a lot applications. The majority features faces were hidden by mask, leaving just quite some, including eyes plus head-region, that’s utilized recognize. This challenge may reduce percentage limited area extract features. Due popularity deep learning many research areas especially vision,In this work, covered system introduced. utilizing Convolutional neural network (CNN), one widely common algorithms. final layer CNN architecture, softmax activation function, was identify facial characteristics after they had been extracted using from masked face's eyes, forehead, brow regions. In Study employ "Extended Yale B database," which has issues with changes placement lighting. additionally, Dataset medical masks. comparison other approaches solving problem, our strategy showed be successful promising accuracy B" 95%.