Face Recognition Model Design Based on Convolutional Neural Networks

Abstract

With the rapid development of computer science and Internet technology, face recognition technology is widely used in such as public security, judicial and criminal investigation, public security, information security and access control system, such as public security system need to find out the criminals in the system library, or from the entrance control system quickly identify and match the identity of relevant personnel information. As a stable, intuitive and highly recognizable biometric feature, human face is being paid more and more attention by researchers. Compared with other bioinformation recognition methods, face recognition is characterized by direct, friendliness and convenience. Users are not easy to resist, and compared with other recognition technologies, they are easy to be accepted by users, so it has received attention and research. This paper designs a face recognition system based on a convolutional neural network. Compared with the traditional face recognition method, the convolutional neural network model does not need manual complex and time-consuming feature extraction algorithm design, only need to design an effective neural network model, and then end-to-end training on a large number of training samples for simple and efficient training, can get a good identification effect. The design uses the target detection algorithm to conduct accurate, real-time, efficient face recognition, and can accurately identify the people in the camera.