A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas

P. Saha, D. Bhattacharjee, B. K. De, M. Nasipuri
{"title":"A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas","authors":"P. Saha, D. Bhattacharjee, B. K. De, M. Nasipuri","doi":"10.1142/s0219467822500498","DOIUrl":null,"url":null,"abstract":"There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.","PeriodicalId":177479,"journal":{"name":"Int. J. Image Graph.","volume":"28 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Image Graph.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s0219467822500498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.
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
基于热变形面部区域的热混合面部表情分析与识别系统
在可见和热态面部表情分析与识别方面有很多研究工作。在这两种模式下已经设计了几个面部表情数据库。然而,对混合面部表情的热红外光谱分析却很少受到重视。在本文中,我们介绍了一个视觉-热混合面部表情数据库(VTBE),该数据库包含具有基本面部表情和混合面部表情的视觉和热面部图像。该数据库包含12种姿势的混合面部表情和自发的6种基本面部表情。本文提出了热表达人脸图像中的变形热面面积(DTFA),并利用该方法对基本表情和混合表情进行了区分分析。在此,结合两种模式和实验的特点,提出了DTFA和变形视觉面部区域(DVFA)的融合,并在这个新的数据库上进行了。然而,为了证明我们提出的方法的有效性,我们使用USTC-NVIE数据库将我们的方法与最先进的方法进行了比较。实验结果表明,我们的方法优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Hybrid Pattern Extraction with Deep Learning-Based Heart Disease Diagnosis Using Echocardiogram Images Certainty-Based Deep Fused Neural Network Using Transfer Learning and Adaptive Movement Estimation for the Diagnosis of Cardiomegaly Deep Ensemble Model for Spam Classification in Twitter via Sentiment Extraction: Bio-Inspiration-Based Classification Model A Systematic Survey on Photorealistic Computer Graphic and Photographic Image Discrimination A Review on Deep Learning Classifier for Hyperspectral Imaging
×
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:604180095
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