HIM‐PyraNet: Hierarchical Attention and Region‐Focused Lightweight Network for Micro‐Expression Recognition

Micro‐expression recognition (MER) is a technology that infers emotions or intentions by analyzing subtle changes in facial expressions. It is often used to identify deception or assist interpersonal communication and holds significant value. Currently, MER technology is advancing rapidly; however, several challenges remain. To address the insufficient capture of local features and high computational complexity in MER, this paper proposes a novel lightweight network architecture. We introduce a Hierarchical Interactive Multi‐scale Pyramid Network (HIM‐PyraNet) that identifies key facial muscle movement regions while considering inherent spatial relationships between facial landmarks, differing from most existing studies using self‐attention mechanisms. HIM‐PyraNet comprises two main components: a Cross‐Region Interaction Attention (CRIA) module focusing on local temporal features and a Multi‐scale Feature Pyramid Fusion (MFPF) module integrating local and global semantics. Specifically, the face is divided into four distinct regions: left eye, right eye, left lip, and right lip. The CRIA module captures local micro‐muscle movements with region‐specific self‐attention, while the MFPF module learns interactions between eye and lip regions. This strategy effectively reduces the processing area for facial MER, successfully builds facial regional collaboration, and retains the local detailed features of expressions while reducing computational parameters. Experiments on SAMM, CASME II, and SMIC datasets show that with only 0.25M parameters, our model achieves 85.31% average recognition accuracy and 75.3 frames per second (FPS) inference speed. Compared with existing methods, this architecture demonstrates higher accuracy with a low parameter count, providing an innovative solution for real‐time micro‐expression analysis on edge devices.