Research on neural network algorithm in artificial intelligence recognition

Abstract

With the rapid development of computer application technology, computer artificial intelligence recognition technology has also made rapid progress. Artificial intelligence was first represented by LISP language, machine theorem proof, etc., and then the intelligent medical treatment and neural network appeared, and now it realizes the popularization of AI. The purpose of this article is to explore the application of neural network algorithms in artificial intelligence recognition. Based on the classic convolutional neural network with LeNet structure, this paper constructs a reasonable convolutional neural network model by deepening and expanding, and uses it in face recognition to conduct face recognition research. LeNet is the origin of CNN, and LeNet is mainly used to identify and classify handwritten characters. This paper uses three networks on the YaleB face database for face recognition, and compares the pooling method of the maximum pooling and average pooling, the misclassification rate of the ReLU incentive function and the tanh incentive function on the three networks.YaleB is a very complete face database, divided into five subsets, ten people, and 64 images per person, including changes in illumination, expressions, and gestures. The tanh function has what is an odd function and has a soft saturation property, while the relu function has a hard saturation property. Analyze whether it can solve the problems caused by illumination factors and occlusion factors in traditional face recognition. Finally, the recognition effect is compared with the traditional face recognition method, and it is concluded that the effect of the traditional face recognition method is not as high as the misclassification rate of 0.0356 based on the convolutional neural network. The recognition method works well. Maximum pooling is also known as maximum reduced pixel sampling, and is more based on the input element map than average pooling.Through the optimization of the algorithm, the face recognition rate is improved, which helps the application and development of artificial intelligence.