CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
{"title":"CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network","authors":"","doi":"10.1007/s41095-023-0369-x","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed <em>C</em><sup>2</sup> activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks. <span> <span> <img alt=\"\" src=\"https://static-content.springer.com/image/MediaObjects/41095_2023_369_Fig1_HTML.jpg\"/> </span> </span></p>","PeriodicalId":37301,"journal":{"name":"Computational Visual Media","volume":null,"pages":null},"PeriodicalIF":17.3000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Visual Media","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s41095-023-0369-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C2 activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks. Abstract Image

CF-DAN:基于交叉融合双注意网络的面部表情识别
摘要 最近,面部表情识别(FER)主要侧重于野外图像,包括人脸遮挡和图像模糊等因素,而不是实验室图像。复杂的野外环境给 FER 带来了新的挑战。为了应对这些挑战,本研究提出了一种交叉融合双注意网络。该网络由三部分组成:(1) 交叉融合分组双注意机制,用于提炼局部特征并获取全局信息;(2) 提出的 C2 激活函数构造方法,即具有三个自由度的片断三次多项式,需要的计算量更少,灵活性和识别能力更强,能较好地解决运行速度慢和神经元失活的问题;(3) 自注意提炼过程与残余连接之间的闭环操作,用于抑制冗余信息,提高模型的泛化能力。在 RAF-DB、FERPlus 和 AffectNet 数据集上的识别准确率分别为 92.78%、92.02% 和 63.58%。实验表明,该模型能为 FER 任务提供更有效的解决方案。
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 求助全文
来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
×
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