Uncertainty-aware Cross-dataset Facial Expression Recognition via Regularized Conditional Alignment

Cross-dataset facial expression recognition (FER) has remained a challenging problem due to the obvious biases caused by diverse subjects and various collection conditions. To this end, domain adaption can be adopted as an effective solution by learning invariant representations across domains (datasets). However, FER requires special consideration of its specific problems e.g., uncertainties caused by ambiguous facial images, and diverse inter- and intra-class relationship. Such uncertainties already exist in single dataset FER, and could be significantly aggravated by enlarged class-wise discrepancies under cross-dataset scenarios. To mitigate this problem, this paper proposes an unsupervised domain adaptation method via regularized conditional alignment for FER, which adversarially reduces domain- and class-wise discrepancies while explicitly dealing with uncertainties within and across domain. Specifically, the proposed method effectively suppresses uncertainties in FER transfer tasks via: 1) semantics-preserving adaptation framework which enforces both domain-invariant learning and class-level semantic consistency between source and target expression data, where discriminative cluster structures are simultaneously retained; 2) auxiliary uncertainty regularization which further constrains the ambiguity of cluster boundaries to guarantee the transferring reliability, thus discouraging the negative transfer brought by divergent facial images. Evaluation experiments on publicly available datasets demonstrate that the proposed method significantly outperforms the current state-of-the-art methods.