Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion
This work addresses micro-expression recognition, a domain-specific problem in computer vision, with incremental improvements over prior methods.
The paper tackled the challenge of recognizing micro-expressions by proposing a dual-branch feature extraction network with parallel attention, achieving 74.67% accuracy on the CASME II dataset and outperforming existing methods like LBP-TOP and MSMMT.
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.