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

Researchers developed a dual-branch neural network for micro-expression recognition that combines residual and Inception networks with parallel attention mechanisms. The method achieved 74.67% accuracy on the CASME II dataset, significantly outperforming existing approaches like LBP-TOP by over 11%.