VGG Network-Based Deep Convoluted Facial Recognition

Deep convolutional neural networks (CNNs), which play a vital role in producing state-of-the-art outcomes in facial recognition technology, have seen considerable developments in recent years. Facial recognition technology has also seen substantial advancements in recent years. This paper proposed Very Deep convolutional Networks (VGGNet) CNN architecture. The proposed method is trained using a labeled face (LFW) in the wild dataset. The advantage of the proposed architecture the tasks are easily replicated any number of times. The accuracy of the proposed method is 87.66%.