Studies advanced in face recognition technology based on deep learning

Abstract

Face recognition technology has always been a hot research topic in the computer vision community, and has developed rapidly in recent years. Face recognition aims to build a model and predict the face identity information in a given image, which has been widely used in various aspects of social life, such as identity authentication, security encryption, human-computer interaction, etc. In order to improve the accuracy and speed of face recognition, and how to maintain good face recognition under the premise of occlusion, many advanced technologies have been proposed. This paper summarizes the face recognition technologies proposed in recent years, and introduces the latest research progress in the field of face recognition from two aspects: traditional face recognition based on manual features and face recognition based on deep learning. Specifically, we first briefly introduce traditional face recognition methods. Second, we introduce the mechanism of traditional Convolutional Neural Networks(Hereinafter referred to as CNN) in face recognition. Finally, we focus on the application of Transformer in the field of face recognition. According to the datasets used by the methods introduced above, the performance of these methods is summarized, the advantages and disadvantages of CNN and Transformer are pointed out, and the future development direction is proposed.