Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion

Mingjie Zhang; Bo Li; Wanting Liu; Hongyan Cui; Yue Li; Qingwen Li; Hong Li; Ge Gao

Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion

Computer Vision and Pattern Recognition 2026-03-02 v1 Artificial Intelligence

Authors: Mingjie Zhang , Bo Li , Wanting Liu , Hongyan Cui , Yue Li , Qingwen Li , Hong Li , Ge Gao

Abstract

Micro-expressions, characterized by transience and subtlety, pose challenges to existing optical flow-based recognition methods. To address this, this paper proposes a dual-branch micro-expression feature extraction network integrated with parallel attention. Key contributions include: 1) a residual network designed to alleviate gradient anishing and network degradation; 2) an Inception network constructed to enhance model representation and suppress interference from irrelevant regions; 3) an adaptive feature fusion module developed to integrate dual-branch features. Experiments on the CASME II dataset demonstrate that the proposed method achieves 74.67% accuracy, outperforming LBP-TOP (by 11.26%), MSMMT (by 3.36%), and other comparative methods.

Keywords

multi-sensor fusion image classification image segmentation

Cite

@article{arxiv.2602.23950,
  title  = {Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion},
  author = {Mingjie Zhang and Bo Li and Wanting Liu and Hongyan Cui and Yue Li and Qingwen Li and Hong Li and Ge Gao},
  journal= {arXiv preprint arXiv:2602.23950},
  year   = {2026}
}

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4 pages, 4 figures,conference paper

Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion

Abstract

Keywords

Cite

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