Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%.