DeepMarkerNet:利用 Duchenne 标记的监督进行自发微笑识别

IF 3.9 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Letters Pub Date : 2024-10-01 DOI:10.1016/j.patrec.2024.09.015
Mohammad Junayed Hasan , Kazi Rafat , Fuad Rahman , Nabeel Mohammed , Shafin Rahman
{"title":"DeepMarkerNet:利用 Duchenne 标记的监督进行自发微笑识别","authors":"Mohammad Junayed Hasan ,&nbsp;Kazi Rafat ,&nbsp;Fuad Rahman ,&nbsp;Nabeel Mohammed ,&nbsp;Shafin Rahman","doi":"10.1016/j.patrec.2024.09.015","DOIUrl":null,"url":null,"abstract":"<div><div>Distinguishing between spontaneous and posed smiles from videos poses a significant challenge in pattern classification literature. Researchers have developed feature-based and deep learning-based solutions for this problem. To this end, deep learning outperforms feature-based methods. However, certain aspects of feature-based methods could improve deep learning methods. For example, previous research has shown that Duchenne Marker (or D-Marker) features from the face play a vital role in spontaneous smiles, which can be useful to improve deep learning performances. In this study, we propose a deep learning solution that leverages D-Marker features to improve performance further. Our multi-task learning framework, named DeepMarkerNet, integrates a transformer network with the utilization of facial D-Markers for accurate smile classification. Unlike past methods, our approach simultaneously predicts the class of the smile and associated facial D-Markers using two different feed-forward neural networks, thus creating a symbiotic relationship that enriches the learning process. The novelty of our approach lies in incorporating supervisory signals from the pre-calculated D-Markers (instead of as input in previous works), harmonizing the loss functions through a weighted average. In this way, our training utilizes the benefits of D-Markers, but the inference does not require computing the D-Marker. We validate our model’s effectiveness on four well-known smile datasets: UvA-NEMO, BBC, MMI facial expression, and SPOS datasets, and achieve state-of-the-art results.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"186 ","pages":"Pages 148-155"},"PeriodicalIF":3.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DeepMarkerNet: Leveraging supervision from the Duchenne Marker for spontaneous smile recognition\",\"authors\":\"Mohammad Junayed Hasan ,&nbsp;Kazi Rafat ,&nbsp;Fuad Rahman ,&nbsp;Nabeel Mohammed ,&nbsp;Shafin Rahman\",\"doi\":\"10.1016/j.patrec.2024.09.015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Distinguishing between spontaneous and posed smiles from videos poses a significant challenge in pattern classification literature. Researchers have developed feature-based and deep learning-based solutions for this problem. To this end, deep learning outperforms feature-based methods. However, certain aspects of feature-based methods could improve deep learning methods. For example, previous research has shown that Duchenne Marker (or D-Marker) features from the face play a vital role in spontaneous smiles, which can be useful to improve deep learning performances. In this study, we propose a deep learning solution that leverages D-Marker features to improve performance further. Our multi-task learning framework, named DeepMarkerNet, integrates a transformer network with the utilization of facial D-Markers for accurate smile classification. Unlike past methods, our approach simultaneously predicts the class of the smile and associated facial D-Markers using two different feed-forward neural networks, thus creating a symbiotic relationship that enriches the learning process. The novelty of our approach lies in incorporating supervisory signals from the pre-calculated D-Markers (instead of as input in previous works), harmonizing the loss functions through a weighted average. In this way, our training utilizes the benefits of D-Markers, but the inference does not require computing the D-Marker. We validate our model’s effectiveness on four well-known smile datasets: UvA-NEMO, BBC, MMI facial expression, and SPOS datasets, and achieve state-of-the-art results.</div></div>\",\"PeriodicalId\":54638,\"journal\":{\"name\":\"Pattern Recognition Letters\",\"volume\":\"186 \",\"pages\":\"Pages 148-155\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167865524002770\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167865524002770","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

在模式分类文献中,如何区分视频中的自发微笑和摆拍微笑是一项重大挑战。研究人员针对这一问题开发了基于特征和基于深度学习的解决方案。为此,深度学习优于基于特征的方法。不过,基于特征的方法的某些方面可以改进深度学习方法。例如,先前的研究表明,人脸的杜氏标记(或 D-Marker)特征在自发微笑中起着至关重要的作用,这对提高深度学习性能很有帮助。在本研究中,我们提出了一种利用 D-Marker 特征进一步提高性能的深度学习解决方案。我们的多任务学习框架被命名为 DeepMarkerNet,它将变压器网络与面部 D 标记的利用整合在一起,以实现准确的微笑分类。与以往的方法不同,我们的方法使用两个不同的前馈神经网络同时预测微笑的类别和相关的面部 D-标记,从而建立了一种共生关系,丰富了学习过程。我们方法的新颖之处在于结合了预先计算的 D-标记的监督信号(而不是之前工作中的输入信号),通过加权平均来协调损失函数。这样,我们的训练利用了 D-标记的优势,但推理不需要计算 D-标记。我们在四个著名的微笑数据集上验证了我们模型的有效性:我们在 UvA-NEMO、BBC、MMI 面部表情和 SPOS 数据集上验证了我们模型的有效性,并取得了最先进的结果。
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
DeepMarkerNet: Leveraging supervision from the Duchenne Marker for spontaneous smile recognition
Distinguishing between spontaneous and posed smiles from videos poses a significant challenge in pattern classification literature. Researchers have developed feature-based and deep learning-based solutions for this problem. To this end, deep learning outperforms feature-based methods. However, certain aspects of feature-based methods could improve deep learning methods. For example, previous research has shown that Duchenne Marker (or D-Marker) features from the face play a vital role in spontaneous smiles, which can be useful to improve deep learning performances. In this study, we propose a deep learning solution that leverages D-Marker features to improve performance further. Our multi-task learning framework, named DeepMarkerNet, integrates a transformer network with the utilization of facial D-Markers for accurate smile classification. Unlike past methods, our approach simultaneously predicts the class of the smile and associated facial D-Markers using two different feed-forward neural networks, thus creating a symbiotic relationship that enriches the learning process. The novelty of our approach lies in incorporating supervisory signals from the pre-calculated D-Markers (instead of as input in previous works), harmonizing the loss functions through a weighted average. In this way, our training utilizes the benefits of D-Markers, but the inference does not require computing the D-Marker. We validate our model’s effectiveness on four well-known smile datasets: UvA-NEMO, BBC, MMI facial expression, and SPOS datasets, and achieve state-of-the-art results.
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
Pattern Recognition Letters
Pattern Recognition Letters 工程技术-计算机:人工智能
CiteScore
12.40
自引率
5.90%
发文量
287
审稿时长
9.1 months
期刊介绍: Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.
期刊最新文献
Personalized Federated Learning on long-tailed data via knowledge distillation and generated features Adaptive feature alignment for adversarial training Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning A unified framework to stereotyped behavior detection for screening Autism Spectrum Disorder Explainable hypergraphs for gait based Parkinson classification
×
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