Research on Face Recognition Algorithm Based on Deep Learning

Wang, Yi; Duan, Xinwei

doi:10.1109/icct52962.2021.9657956

Cited by 2 publications

(1 citation statement)

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“…Compared with fingerprint recognition, iris recognition and other recognition devices, face recognition has obvious hardware basic advantages. And the price of face recognition equipment has significant advantages compared with the price of other recognition devices, and the price of face recognition equipment belongs to the acceptable range of users [7]. At the same time, with the improvement of the monitoring system, it can also be used to find the lost property and accurately crack down on the crime of population trafficking and other functions, because the surveillance can save the video and restore the scene at that time, which is impossible for other recognition methods.…”

Section: Concurrency Equipment Requirements Are Singlementioning

confidence: 99%

Face Recognition Model Design Based on Convolutional Neural Networks

Zhang

2022

Scientific Programming

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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.

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Section: Concurrency Equipment Requirements Are Singlementioning

confidence: 99%