{"title":"基于卷积神经网络的面部表情识别算法研究","authors":"Xiaobo Zhang, Yuliang Yang, Linhao Zhang, Wanchong Li, Shuai Dang, Peng Wang, Mengyu Zhu","doi":"10.1109/WOCC.2019.8770616","DOIUrl":null,"url":null,"abstract":"A network model for facial expression recognition is designed and named DI-FERNet in this paper. The network uses depth-wise separable convolution, dilated convolution and residual module to build the network structure. This paper uses MTCNN to perform face alignment processing on the pictures in the dataset. A large number of experiments are carried out on the selected expression datasets KDEF and RAF. The test accuracy on KDEF is 97.2% and on the RAF is 77.1%.","PeriodicalId":285172,"journal":{"name":"2019 28th Wireless and Optical Communications Conference (WOCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on Facial Expression Recognition Algorithm Based on Convolutional Neural Network\",\"authors\":\"Xiaobo Zhang, Yuliang Yang, Linhao Zhang, Wanchong Li, Shuai Dang, Peng Wang, Mengyu Zhu\",\"doi\":\"10.1109/WOCC.2019.8770616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A network model for facial expression recognition is designed and named DI-FERNet in this paper. The network uses depth-wise separable convolution, dilated convolution and residual module to build the network structure. This paper uses MTCNN to perform face alignment processing on the pictures in the dataset. A large number of experiments are carried out on the selected expression datasets KDEF and RAF. The test accuracy on KDEF is 97.2% and on the RAF is 77.1%.\",\"PeriodicalId\":285172,\"journal\":{\"name\":\"2019 28th Wireless and Optical Communications Conference (WOCC)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 28th Wireless and Optical Communications Conference (WOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOCC.2019.8770616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 28th Wireless and Optical Communications Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC.2019.8770616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Facial Expression Recognition Algorithm Based on Convolutional Neural Network
A network model for facial expression recognition is designed and named DI-FERNet in this paper. The network uses depth-wise separable convolution, dilated convolution and residual module to build the network structure. This paper uses MTCNN to perform face alignment processing on the pictures in the dataset. A large number of experiments are carried out on the selected expression datasets KDEF and RAF. The test accuracy on KDEF is 97.2% and on the RAF is 77.1%.
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