Facial recognition technology, a form of artificial intelligence, identifies individuals by capturing and analyzing facial images against a database. It has several use cases, including face verification, closed-set recognition, and open-set recognition, each with different requirements and challenges. The technology operates through a pipeline that involves detecting faces, aligning them, extracting features using deep neural networks, and matching these features against a database of known faces. The effectiveness of facial recognition depends on factors such as the angle of the face, known as yaw, and is influenced by the quality of the training set and computational resources. While deep learning can enhance each stage of the recognition process, the system's performance is determined by how well it manages variations in facial images and the match metric used to compare facial vectors.