Abstract
Micro-expressions (MEs), characterized by their brief duration and subtle facial muscle movements, pose significant challenges for accurate recognition. These ultra-fast signals, typically captured by high-speed vision sensors, require specialized computational methods to extract spatio-temporal features effectively. In this study, we propose a lightweight dual-stream network with an adaptive strategy for efficient ME recognition. Firstly, a motion magnification network based on transfer learning is employed to magnify the motion states of facial muscles in MEs. This process can generate additional samples, thereby expanding the training set. To effectively capture the dynamic changes of facial muscles, dense optical flow is extracted from the onset frame and the magnified apex frame, thereby obtaining magnified dense optical flow (MDOF). Subsequently, we design a dual-stream spatio-temporal network (DSTNet), using the magnified apex frame and MDOF as inputs for the spatial and temporal streams, respectively. An adaptive strategy that dynamically adjusts the magnification factor based on the top-1 confidence is introduced to enhance the robustness of DSTNet. Experimental results show that our proposed method outperforms existing methods in terms of F1-score on the SMIC, CASME II, SAMM, and composite dataset, as well as in cross-dataset tasks. Adaptive DSTNet significantly enhances the handling of sample imbalance while demonstrating robustness and featuring a lightweight design, indicating strong potential for future edge sensor deployment.