M2E: Lightweight Motion-to-Emotion Inference for Edge-Oriented Micro-Expression Recognition

Micro-expression recognition (MER) requires capturing subtle facial movements with low computational cost and providing interpretable decision evidence for edge deployment, yet existing deep models typically learn latent representations from facial images, incurring substantial resource overhead and offering limited process-level interpretability. In this paper, we propose M2E, a lightweight MER framework that encodes compensated facial dynamics as a compact, semantically explicit 12-dimensional regional representation. M2E suppresses head-motion interference, transforms compensated onset–apex optical flow into FACS-guided regional features, and employs an RBF-SVM with class weighting for efficient and interpretable classification. Under the MEGC2019 Composite Database Evaluation protocol, the lightweight anchor-based configuration achieves 76.94% UF1 and 79.99% UAR on the 3DB dataset, outperforming LAENet, the most lightweight existing baseline, by 1.26 and 5.94 percentage points, respectively, with lower computational overhead. When equipped with MEFlowNet, a more computationally intensive learning-based optical-flow compensation network, M2E further achieves 79.80% UF1 and 83.20% UAR on the 3DB dataset and matches or surpasses high-capacity expert models on several benchmark metrics. Raspberry Pi experiments, ablation studies, and SHAP analyses further demonstrate its edge applicability, component effectiveness, and process-level interpretability, while evaluations on MMEW and CK+ show its cross-dataset and cross-domain generalization. Code and data are available at https://anonymous.4open.science/r/M2E-F27.