Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Abstract

Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliable global–local representation learning and dynamic label weighting. Our framework incorporates two key modules: the Pseudo-Complementary Label Generation (PCLG) module, which leverages pseudo-labels and complementary labels obtained using a credibility threshold to learn domain-invariant global and local features, and the Label Dynamic Weight Matching (LDWM) module, which assesses the learning difficulty of each category and adaptively assigns corresponding label weights, thereby enhancing the classification performance in the target domain. We evaluate our approach through extensive experiments and analyses on multiple public datasets, including RAF-DB, FER2013, CK+, JAFFE, SFW2.0, and ExpW. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, with an average accuracy improvement of 3.5% across the five datasets.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…This experiment demonstrated that students based their answers on discerning distinctive areas in the two bodies, mirroring the functionality of the LFP algorithm. It identifies distinguishing areas in images for subsequent utilization in machine learning [51], [52], [53].…”

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

“…Hence, it is evident that CNN models are highly beneficial for facial expression recognition tasks. Gao et al [11] presented cross-domain facial expression recognition (CD-FER). CD-FER integrates features across domains and incorporates dynamic label weighting.…”