Cross-Domain Sample Relationship Learning for Facial Expression Recognition

Cross-domain facial expression recognition is confronted by the problem of the large distribution discrepancy and samples inconsistencies between the source domain and target domain. To solve this problem, we propose a cross-domain sample relationship learning (CSRL) method that explores useful intrinsic sample relationships of two domains to narrow the domain discrepancy. Specifically, during the training stage, we first design inter-domain sample transformers to explore the sample similarity relationships between the source and target domains, and then deploy intra-domain sample transformers to capture the internal similar structure of the samples in each domain. Thus dual sample relationships can be learned to align the cross-domain similar samples and preserve the domain-specific information, which can facilitate both the inter-domain invariant features and intra-domain invariant features learning. Subsequently, we design a joint alignment strategy by simultaneously deploying the feature distribution alignment and cross-domain sample relationship learning. Thus, both local similar samples and global domain distribution of two domains can be well aligned to enhance the generalization ability of the model. Experimental results on several benchmark databases show the superiority of CSRL over some state-of-the-art methods.