Year: 2025
Type: article
Abstract: Micro-expression recognition (MER), as an important branch of intelligent visual sensing, enables the analysis of subtle facial movements for applications in emotion understanding, human-computer interaction and security monitoring. However, existing methods struggle to capture fine-grained spatiotemporal dynamics under limited data and computational resources, making them difficult to deploy in real-world sensing systems. To address this limitation, we propose HMRM, a hybrid motion and region-fused Mamba network designed for efficient and accurate MER. HMRM enhances motion representation through a hybrid feature augmentation module that integrates gated recurrent unit (GRU)-attention optical flow estimation with a regional MotionMix enhancement strategy to increase motion diversity. Furthermore, it employs a grained Mamba encoder to achieve lightweight and effective long-range temporal modeling. Additionally, a regions feature fusion strategy is introduced to strengthen the representation of localized expression dynamics. Experiments on multiple MER benchmark datasets demonstrate that HMRM achieves state-of-the-art performance with strong generalization and low computational cost, highlighting its potential for integration into compact, real-time visual sensing and emotion analysis systems.
Source: Sensors
DOI: 10.3390/s25247672
Authors: Zhe Guo, Yi Liu, Rui Luo, Jiayi Liu, Lan Wei
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G7506404467
OpenAlex ID: W4417456715