Microexpression Recognition Algorithm Based on a Two-Branch Lightweight Network

One of the common challenges encountered in microexpression recognition using convolutional neural networks is the heightened complexity caused by increased accuracy. To address this challenge, this study introduces an enhanced lightweight dual-stream attention network, called the enhanced dual- stream MISEViT network (EDSMISEViTNet), for microexpression recognition. First, microexpression samples are preprocessed, and peak frames are extracted as spatial features. Additionally, the TV-L1 optical flow method is used to extract the temporal features between the start frame and the vertex frame of each sample. Furthermore, this study improves the MobileViT network by designing an MI module that combines Inception and SE modules and introduces an attention module for efficient feature extraction. Temporal and spatial features are separately fed into this network, and the resultant features are concatenated, fused, and subsequently subjected to classification. To enhance precision, the CASME II, SAMM, and SMIC datasets are combined into a composite dataset for experimentation. The results reveal that the proposed algorithm model requires a training parameter count of only 3.9x10(6) and processes a single sample in just 71.8 ms. Compared with the existing methods, this approach achieves excellent accuracy while maintaining a low parameter count.