{"title":"基于面部表情的实时情绪识别系统的开发","authors":"Taneja, Yogesh","doi":"10.1109/RDCAPE52977.2021.9633406","DOIUrl":null,"url":null,"abstract":"The face depicts the identity of the individual and draws attention towards their psychological state. The face shows a wide range of expressions in reaction to various instincts and their surrounding environment. In this paper, we determine the algorithm used to recognize the facial expression of a person. Four different types of expressions are fed to the system. Machine Learning and Deep Learning-based models are trained with datasets to unveil human expressions. The experimental result from the network is successful as it matches with the virtual learning of the student. The sample is trained and tested, through which the machine builds a predictive model. This model achieved an accuracy of 99%.","PeriodicalId":424987,"journal":{"name":"2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Development of A Real-Time Emotion Recognition System using Facial Expressions\",\"authors\":\"Taneja, Yogesh\",\"doi\":\"10.1109/RDCAPE52977.2021.9633406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The face depicts the identity of the individual and draws attention towards their psychological state. The face shows a wide range of expressions in reaction to various instincts and their surrounding environment. In this paper, we determine the algorithm used to recognize the facial expression of a person. Four different types of expressions are fed to the system. Machine Learning and Deep Learning-based models are trained with datasets to unveil human expressions. The experimental result from the network is successful as it matches with the virtual learning of the student. The sample is trained and tested, through which the machine builds a predictive model. This model achieved an accuracy of 99%.\",\"PeriodicalId\":424987,\"journal\":{\"name\":\"2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RDCAPE52977.2021.9633406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RDCAPE52977.2021.9633406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of A Real-Time Emotion Recognition System using Facial Expressions
The face depicts the identity of the individual and draws attention towards their psychological state. The face shows a wide range of expressions in reaction to various instincts and their surrounding environment. In this paper, we determine the algorithm used to recognize the facial expression of a person. Four different types of expressions are fed to the system. Machine Learning and Deep Learning-based models are trained with datasets to unveil human expressions. The experimental result from the network is successful as it matches with the virtual learning of the student. The sample is trained and tested, through which the machine builds a predictive model. This model achieved an accuracy of 99%.