A Semi-Lightweight Multi-Feature Integration Architecture for Micro-Expression Recognition

Abstract

Micro-expressions, fleeting involuntary facial cues lasting under half a second, reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy. Real-time recognition on resource-constrained embedded devices remains challenging, as current methods struggle to balance performance and efficiency. This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy. Unlike prior simplistic feature fusion techniques, our novel multi-feature fusion strategy leverages temporal, spatial, and differential features to better capture dynamic changes. Enhanced by Residual Network (ResNet) architecture with channel and spatial attention mechanisms, the model improves feature representation while maintaining a lightweight design. Evaluations on SMIC, CASME II, SAMM, and their composite dataset show superior performance in Unweighted F1 Score (UF1) and Unweighted Average Recall (UAR), alongside faster detection speeds compared to existing algorithms.