{"title":"Graph-Diffusion-Based Domain-Invariant Representation Learning for Cross-Domain Facial Expression Recognition","authors":"Run Wang;Peng Song;Wenming Zheng","doi":"10.1109/TCSS.2024.3355113","DOIUrl":null,"url":null,"abstract":"The precondition that most of the existing facial expression recognition (FER) algorithms have succeeded lies in that the training (source) and test (target) samples are independent of each other and identically distributed. However, it is too strict to satisfy this precondition in the real-world. To this end, we propose a novel graph-diffusion-based domain-invariant representation learning (GDRL) model for the cross-domain FER scenario where there exist distribution shifts between various domains. Specifically, a low-dimensional space mapping strategy is first adopted to diminish the domain mismatch. Then, by skillfully combining the local graph embedding and affinity graph diffusion, the local geometric structures can be effectively modeled and the deeper higher-order relationships of samples from various domains can be captured. In addition, in order to better guide the transfer process and learn a more discriminative and invariant representation, we take into account the label consistency. Experimental results on four laboratory-controlled databases and two in-the-wild databases demonstrate that our proposed model can yield better recognition performance compared with state-of-the-art domain adaptation methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10418230/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
The precondition that most of the existing facial expression recognition (FER) algorithms have succeeded lies in that the training (source) and test (target) samples are independent of each other and identically distributed. However, it is too strict to satisfy this precondition in the real-world. To this end, we propose a novel graph-diffusion-based domain-invariant representation learning (GDRL) model for the cross-domain FER scenario where there exist distribution shifts between various domains. Specifically, a low-dimensional space mapping strategy is first adopted to diminish the domain mismatch. Then, by skillfully combining the local graph embedding and affinity graph diffusion, the local geometric structures can be effectively modeled and the deeper higher-order relationships of samples from various domains can be captured. In addition, in order to better guide the transfer process and learn a more discriminative and invariant representation, we take into account the label consistency. Experimental results on four laboratory-controlled databases and two in-the-wild databases demonstrate that our proposed model can yield better recognition performance compared with state-of-the-art domain adaptation methods.
大多数现有面部表情识别(FER)算法取得成功的前提条件是训练样本(源样本)和测试样本(目标样本)相互独立且分布相同。然而,在现实世界中满足这一前提条件过于严格。为此,我们针对不同领域间存在分布偏移的跨领域 FER 场景,提出了一种基于图扩散的新型领域不变表示学习(GDRL)模型。具体来说,首先采用低维空间映射策略来减少领域不匹配。然后,通过巧妙地结合局部图嵌入和亲和图扩散,可以有效地对局部几何结构进行建模,并捕捉到来自不同域的样本之间更深层次的高阶关系。此外,为了更好地指导转移过程并学习更具区分性和不变性的表示,我们还考虑了标签一致性。在四个实验室控制数据库和两个野生数据库上的实验结果表明,与最先进的领域适应方法相比,我们提出的模型能产生更好的识别性能。
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.