A Benchmark for Incremental Micro-expression Recognition
This work addresses the need for incremental learning in micro-expression recognition, which is crucial for applications in fields like psychology and security, but it is incremental as it focuses on benchmarking rather than novel algorithmic advances.
The paper tackles the problem of micro-expression recognition in real-world scenarios with evolving data streams by introducing the first benchmark for incremental learning in this domain, including formulated settings, curated datasets, testing protocols, and baseline methods with evaluation results.
Micro-expression recognition plays a pivotal role in understanding hidden emotions and has applications across various fields. Traditional recognition methods assume access to all training data at once, but real-world scenarios involve continuously evolving data streams. To respond to the requirement of adapting to new data while retaining previously learned knowledge, we introduce the first benchmark specifically designed for incremental micro-expression recognition. Our contributions include: Firstly, we formulate the incremental learning setting tailored for micro-expression recognition. Secondly, we organize sequential datasets with carefully curated learning orders to reflect real-world scenarios. Thirdly, we define two cross-evaluation-based testing protocols, each targeting distinct evaluation objectives. Finally, we provide six baseline methods and their corresponding evaluation results. This benchmark lays the groundwork for advancing incremental micro-expression recognition research. All source code used in this study will be publicly available at https://github.com/ZhengQinLai/IMER-benchmark.