Incremental Micro-Expression Recognition: A Benchmark

Micro-expression recognition (MER) plays a pivotal role in understanding hidden emotions. While traditional methods assume static datasets, real-world scenarios require adapting to continuously evolving data streams. To this end, we introduce the first benchmark specifically designed for Incremental Micro-Expression Recognition (IMER). Our contributions include: Firstly, we formulate a composite class-domain incremental learning setting and construct a sequential benchmark from five representative datasets with carefully curated learning orders to reflect real-world scenarios. Secondly, we establish robust evaluation protocols with a fold-binding strategy to ensure rigorous and feasible cross-session validation, using comprehensive metrics and novel cross-domain visualizations to diagnose performance. Thirdly, we propose Mahalanobis Refinement (MR), a two-stage approach that leverages accumulated second-order statistics for stability and Mahalanobis-constrained refinement for plasticity. Extensive experiments demonstrate that MR significantly outperforms state-of-the-art baselines, effectively balancing the stability-plasticity dilemma. This work lays the foundation for scalable and adaptive micro-expression analysis. All source codes will be released.