{"title":"一种用于价态表情分类的局部二值模式特征描述子模型","authors":"Ruth Agada, Jie Yan","doi":"10.1109/ICMLA.2015.185","DOIUrl":null,"url":null,"abstract":"Recognition of spontaneous emotion would significantly influence human-computer interaction and emotion-related studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Model of Local Binary Pattern Feature Descriptor for Valence Facial Expression Classification\",\"authors\":\"Ruth Agada, Jie Yan\",\"doi\":\"10.1109/ICMLA.2015.185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recognition of spontaneous emotion would significantly influence human-computer interaction and emotion-related studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.\",\"PeriodicalId\":288427,\"journal\":{\"name\":\"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)\",\"volume\":\"2006 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2015.185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2015.185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Model of Local Binary Pattern Feature Descriptor for Valence Facial Expression Classification
Recognition of spontaneous emotion would significantly influence human-computer interaction and emotion-related studies in many related fields. This paper endeavors to explore a holistic method for detecting emotional facial expressions by examining local features. In recent years, examining local features has gained traction for nuanced expression detection. The local binary pattern is one such technique. Using the modified LBP adds a discriminating factor to the examined feature via the addition of an edge detector. Hence, the edge based local binary pattern for the extraction of features in the human face. Using this method, the extracted feature is classified into its valence classes (positive and negative) using an SVM classifier.