A Benchmark for Incremental Micro-expression Recognition

TL;DR

This paper introduces the first benchmark for incremental micro-expression recognition, including datasets, evaluation protocols, and baseline methods, to advance research in adaptive emotion recognition systems.

Contribution

It formulates the incremental learning setting for micro-expression recognition, provides curated datasets and evaluation protocols, and offers baseline results to facilitate future research.

Findings

Six baseline methods evaluated on the benchmark.

Two cross-evaluation testing protocols established.

Source code made publicly available.

Abstract

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.