論文ID: 2025EDL8020
In ideal facial expression recognition (FER) tasks, the training and test data are assumed to share the same distribution. However, in reality, they are often sourced from different domains, which follow different feature distributions and would seriously impair the recognition performance. In this letter, we present a novel Dynamic Graph-Guided Domain-Invariant Feature Representation (DG-DIFR) method, which addresses the issue of distribution shifts across different domains. First, we learn a robust common subspace to minimize the data distribution differences, facilitating the extraction of invariant feature representations. Concurrently, the retargeted linear regression is employed to enhance the discrimination of the proposed model. Furthermore, a maximum entropy based dynamic graph is further introduced to maintain the topological structure information in the low-dimensional subspace. Finally, numerous experiments conducted on four benchmark datasets confirm the superiority of the proposed method over state-of-the-art methods.
