In light of its uses in security, authentication systems, and user Interaction platforms, face recognition technology has attracted a lot of attention lately. For face detection and recognition in one of my projects, I experimented with a hybrid technique that used the VGG-16 model and MTCNN (Multi-task Cascaded Convolutional Networks). This article explores the architectures of these models, how they are optimized for best performance, and how they cooperate to produce effective face recognition. Recognizing VGG-16's Function in Face Recognition Popular deep learning architecture VGG-16 is renowned for its efficiency and ease of use. It comprises of 16 layers,…