The Study and Application of Facial Recognition Models

Abstract

Facial recognition technology is a biometric application that uniquely identifies or authenticates a person by comparing and analyzing facial features based on the width of a person's face. Face recognition technology has gained popularity recently thanks to the development and invention of artificial intelligence and other technologies. The traditional methods of face recognition technology development—the method based on geometric features and the method based on 3D models—as well as the deep learning Convolutional Neural Network (CNN) model VGG-Face for face recognition—are analyzed, compared, and described in this paper. It also provides a detailed description of face recognition technology. In this work, accuracy and TAR values are employed for geometric feature matching by comparing the metric results of each method on a dataset for better comparison. Our results show that while both traditional geometric feature modeling methods and 3D modeling methods can achieve good accuracy, however, they do not perform as well as humans in terms of accuracy. The Vgg-Face based on the deep learning model completely exceeds the 3D model method in terms of accuracy and TAR value, and is even more accurate than humans.