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.
Abstract (translated)
微表情由于其短暂性和微妙性，对现有的基于光学流的识别方法构成了挑战。为解决这一问题，本文提出了一种集成了并行注意力机制的双分支微表情特征提取网络。主要贡献包括：1）设计了一个残差网络来缓解梯度消失和网络退化的问题；2）构建了一个Inception网络以增强模型表示能力，并抑制无关区域的干扰；3）开发了一个自适应特征融合模块，用于整合双分支特征。在CASME II数据集上的实验表明，所提出的方法实现了74.67%的准确率，优于LBP-TOP（高出11.26%）、MSMMT（高出3.36%）和其他比较方法。
URL
https://arxiv.org/abs/2602.23950
