基于数据挖掘的面部表情识别系统

Hazar Mliki, Nesrine Fourati, Mohamed Hammami, H. Ben-Abdallah
{"title":"基于数据挖掘的面部表情识别系统","authors":"Hazar Mliki, Nesrine Fourati, Mohamed Hammami, H. Ben-Abdallah","doi":"10.3233/978-1-61499-330-8-185","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected con- tours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the per- formance of our proposed solution under varying factors.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":" 83","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Data Mining-based Facial Expressions Recognition System\",\"authors\":\"Hazar Mliki, Nesrine Fourati, Mohamed Hammami, H. Ben-Abdallah\",\"doi\":\"10.3233/978-1-61499-330-8-185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected con- tours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the per- formance of our proposed solution under varying factors.\",\"PeriodicalId\":322432,\"journal\":{\"name\":\"Scandinavian Conference on AI\",\"volume\":\" 83\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scandinavian Conference on AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/978-1-61499-330-8-185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scandinavian Conference on AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/978-1-61499-330-8-185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

本文介绍了一种新的面部表情分析系统,该系统能够自动识别面部表情,管理面部表情强度的变化,减少面部表情类别之间的怀疑和混淆。本文提出了一种利用向量场卷积(VFC)技术高效分割人脸特征轮廓的新方法。根据检测到的轮廓图,提取与面部表情变形相关的面部特征点。然后通过数据挖掘技术建立了一组检测点之间的距离模型来定义预测规则。通过实验研究,评估了该方案在不同因素下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Data Mining-based Facial Expressions Recognition System
In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected con- tours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the per- formance of our proposed solution under varying factors.
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
期刊最新文献
Goal-driven, assistive agents for instructing and guiding user activities On Associative Confounder Bias Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees Revisiting Inner Entanglements in Classical Planning Error AMP Chain Graphs
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1