双分支网络融合注意力机制，提升微表情识别准确率
Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion
- 采用双分支结构分别提取局部与全局特征
- 在CASME II数据集上达到74.67%准确率
- 适合需要高精度微表情分析的研究场景
微表情具有瞬时性和细微性，给基于光流的识别方法带来挑战。为此，本文提出一种集成并行注意力的双分支微表情特征提取网络。主要贡献包括：1）设计残差网络以缓解梯度消失与网络退化问题；2）构建Inception网络以增强模型表征能力并抑制无关区域干扰；3）开发自适应特征融合模块以整合双分支特征。在CASME II数据集上的实验表明，所提方法达到74.67%的识别准确率，优于LBP-TOP（提升11.26%）、MSMMT（提升3.36%）及其他对比方法。
原文摘要 · Abstract (English)
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.
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