{"title":"基于合成图像和深度度量学习的身份感知面部表情识别方法","authors":"Siyuan Zhang, Shiming Xiao, Peng Zhang, Wei Huang","doi":"10.3724/sp.j.1089.2021.18462","DOIUrl":null,"url":null,"abstract":": Facial expression recognition (FER) is a challenging task because the external environment and identity characteristics could affect the classification results directly. To settle down the above-mentioned challenges, this paper proposed an identity-aware facial expression recognition method which combined images synthesis techniques and deep metric learning, and made facial images features compared then clas-sified by creating expression groups under the same identity in FER task. There are three parts in our method. The first part is a generative adversarial network, which aims to learn expression information and synthesis the expression groups. the state-of-the-art methods, the experimental results confirmed that the proposed-method was effective and progressive in FER task.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identity-Aware Facial Expression Recognition Method Based on Synthesized Images and Deep Metric Learning\",\"authors\":\"Siyuan Zhang, Shiming Xiao, Peng Zhang, Wei Huang\",\"doi\":\"10.3724/sp.j.1089.2021.18462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": Facial expression recognition (FER) is a challenging task because the external environment and identity characteristics could affect the classification results directly. To settle down the above-mentioned challenges, this paper proposed an identity-aware facial expression recognition method which combined images synthesis techniques and deep metric learning, and made facial images features compared then clas-sified by creating expression groups under the same identity in FER task. There are three parts in our method. The first part is a generative adversarial network, which aims to learn expression information and synthesis the expression groups. the state-of-the-art methods, the experimental results confirmed that the proposed-method was effective and progressive in FER task.\",\"PeriodicalId\":52442,\"journal\":{\"name\":\"计算机辅助设计与图形学学报\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"计算机辅助设计与图形学学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3724/sp.j.1089.2021.18462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机辅助设计与图形学学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3724/sp.j.1089.2021.18462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Identity-Aware Facial Expression Recognition Method Based on Synthesized Images and Deep Metric Learning
: Facial expression recognition (FER) is a challenging task because the external environment and identity characteristics could affect the classification results directly. To settle down the above-mentioned challenges, this paper proposed an identity-aware facial expression recognition method which combined images synthesis techniques and deep metric learning, and made facial images features compared then clas-sified by creating expression groups under the same identity in FER task. There are three parts in our method. The first part is a generative adversarial network, which aims to learn expression information and synthesis the expression groups. the state-of-the-art methods, the experimental results confirmed that the proposed-method was effective and progressive in FER task.