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Micro-expression recognition based on dataset balance and local connected bi-branch network
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DOI：10.1016/j.image.2026.117480.png)
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Abstract
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Micro-expressions are subtle and transient facial movements that reveal underlying human emotions, and they hold significant research and application value in fields such as public safety, criminal investigation, and clinical diagnosis. However, due to their fleeting duration and low intensity, existing micro-expression datasets are limited in size and suffer from severe class imbalance, which poses great challenges for reliable recognition. In this paper, we propose an assessment-based re-sampling (ASR) strategy to augment micro-expression data and alleviate category imbalance. Specifically, we first employ semi-supervised self-training on the original dataset to learn an assessment model with both high accuracy and high recall. This model is then used to evaluate frames in micro-expression video sequences (excluding those in the training set). The non-apex frames identified through this assessment are subsequently selected to directly expand the underrepresented classes. Furthermore, we design a locally connected bi-branch network (LCB) for micro-expression recognition. In this network, the high-frequency components of micro-expression frames are extracted to capture weak facial muscle movements and combined with global information as complementary input. We conduct extensive experiments on three benchmark datasets, CASME, CASME II, and SAMM. The results demonstrate that our method is both effective and competitive, achieving an accuracy of 90.23% on the SAMM dataset.
Keywords:
Micro-expression recognition
Dataset balance
Apex-frame
Attention mechanism
Local connected bi-branch network
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