Masked Face Recognition Using Convolutional Neural Networks

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