[2501.19111] A Benchmark for Incremental Micro-expression Recognition

Computer Science > Computer Vision and Pattern Recognition arXiv:2501.19111 (cs) [Submitted on 31 Jan 2025 (v1), last revised 30 Mar 2026 (this version, v3)] Title:A Benchmark for Incremental Micro-expression Recognition Authors:Zhengqin Lai, Xiaopeng Hong, Yabin Wang, Xiaobai Li View a PDF of the paper titled A Benchmark for Incremental Micro-expression Recognition, by Zhengqin Lai and 3 other authors View PDF HTML (experimental) 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 p...