{"title":"基于深度学习的情绪检测","authors":"Yuwei Chen, Jia-Zhou He","doi":"10.36227/techrxiv.18866159","DOIUrl":null,"url":null,"abstract":"Since the deep learning methods used in current face recognition do not balance well between recognition rate and recognition speed, the present work proposed a face expression recognition model based on multilayer feature fusion with lightweight convolutional networks. The model is tested on two commonly used real expression datasets, FER- 2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets.","PeriodicalId":67799,"journal":{"name":"电脑和通信(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Deep learning-based emotion detection\",\"authors\":\"Yuwei Chen, Jia-Zhou He\",\"doi\":\"10.36227/techrxiv.18866159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the deep learning methods used in current face recognition do not balance well between recognition rate and recognition speed, the present work proposed a face expression recognition model based on multilayer feature fusion with lightweight convolutional networks. The model is tested on two commonly used real expression datasets, FER- 2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets.\",\"PeriodicalId\":67799,\"journal\":{\"name\":\"电脑和通信(英文)\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"电脑和通信(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.36227/techrxiv.18866159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"电脑和通信(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.36227/techrxiv.18866159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Since the deep learning methods used in current face recognition do not balance well between recognition rate and recognition speed, the present work proposed a face expression recognition model based on multilayer feature fusion with lightweight convolutional networks. The model is tested on two commonly used real expression datasets, FER- 2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets.
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