Alzahrani Ahmed Omar, Alghamdi Ahmed Mohammed, Ashraf M Usman, Ilyas Iqra, Sarwar Nadeem, Alzahrani Abdulrahman, Alarood Alaa Abdul Salam
Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi Arabia.
Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Makkah, Saudi Arabia.
PeerJ Comput Sci. 2025 May 9;11:e2866. doi: 10.7717/peerj-cs.2866. eCollection 2025.
Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning; however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model's effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively.
由于域偏移，域目标中的变化最近给面部表情识别任务带来了重大挑战。当前的方法主要集中在采用全局特征以实现域不变学习；然而，在不同域之间转移局部特征仍然是一个持续存在的挑战。此外，在目标数据集上进行训练时，由于判别监督不足，这些方法在目标域中往往会出现特征表示能力下降的问题。为了应对这些挑战，我们提出了一种用于面部表情识别的动态跨域双注意力网络。我们的模型专门设计用于通过全局和局部对抗学习的单独模块来学习域不变特征。我们还引入了一个语义感知模块来生成伪标签，该模块从全局和局部特征中计算语义标签。我们通过在真实世界情感面孔数据库（RAF-DB）、FER-PLUS、AffectNet、野外表情（ExpW）、SFEW 2.0和日本女性面部表情（JAFFE）数据集上进行广泛实验来评估我们模型的有效性。结果表明，我们的方案分别以93.18、92.35、82.13、78.37、72.47、70.68的识别准确率优于现有的最先进方法。
