Asymmetric Adversarial-based Feature Disentanglement Learning for Cross-Database Micro-Expression Recognition

Recently, micro-expression recognition (MER) has gained tremendous progress. However, most methods are based on individual-database micro-expression recognition and are difficult to generalize into complicated scenarios. Therefore, cross-database micro-expression recognition (CDMER) has drawn growing attention due to its robustness and generalizability. In this paper, we propose a novel CDMER algorithm with asymmetric adversarial-based feature disentanglement learning, which implements the disentanglement of domain features and emotion features aiming to learn domain-invariant and discriminative representation. Furthermore, to facilitate the feature disentanglement learning, a Domain Information Filtering (DF) module is designed to filter out the domain component of the micro-expression features (emotion feature). Extensive experiments on the SMIC and CASME II databases have shown that our proposed method outperforms the state-of-the-art method and has superior performance against excessive domain discrepancies.