一种用于价态表情分类的局部二值模式特征描述子模型

Ruth Agada, Jie Yan
{"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}
引用次数: 3

摘要

自发情绪的识别将对人机交互和许多相关领域的情绪相关研究产生重大影响。本文试图探索一种通过检测局部特征来检测面部情绪的整体方法。近年来,局部特征检测在细微差别表情检测中得到了广泛的应用。局部二进制模式就是这样一种技术。使用改进的LBP,通过添加边缘检测器,为被检测的特征增加了一个判别因子。因此,基于边缘的局部二值模式用于人脸特征的提取。利用该方法,利用SVM分类器将提取的特征分类为正价类和负价类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信