{"title":"Research on emotion recognition algorithm based on improved BP neural network","authors":"Qingqing Wang, Jing Wang, Mei-li Zhu","doi":"10.1088/1742-6596/1976/1/012002","DOIUrl":null,"url":null,"abstract":"The application of small robots in the production of freeze frame animation can speed up the production progress and improve the quality of production. Small robots will play the role of intelligent agents. It is necessary to recognize their emotions for the promotion of animation films. With the development of intelligent technology, BP neural network has been applied to the field of emotion recognition because of its advantages of self-learning, self-adaptive and self-organization. In this paper, the parameters of BP neural network are adjusted and improved to avoid the defects of BP neural network in emotion recognition. Then, the parameters and structure of the network are designed according to the emotion category. Then a three-layer BP neural network with 4 input nodes, 13 hidden layer nodes and 4 output nodes is constructed. Finally, it is applied to emotion recognition, and 10 groups of data are selected from the training samples for detection, and the diagnostic accuracy is 80%.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":"117 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics: Conference Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1742-6596/1976/1/012002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The application of small robots in the production of freeze frame animation can speed up the production progress and improve the quality of production. Small robots will play the role of intelligent agents. It is necessary to recognize their emotions for the promotion of animation films. With the development of intelligent technology, BP neural network has been applied to the field of emotion recognition because of its advantages of self-learning, self-adaptive and self-organization. In this paper, the parameters of BP neural network are adjusted and improved to avoid the defects of BP neural network in emotion recognition. Then, the parameters and structure of the network are designed according to the emotion category. Then a three-layer BP neural network with 4 input nodes, 13 hidden layer nodes and 4 output nodes is constructed. Finally, it is applied to emotion recognition, and 10 groups of data are selected from the training samples for detection, and the diagnostic accuracy is 80%.