关键词: 微表情识别, 光流分区, 分层Transformer, 深度学习, 特征重组

Abstract: To address the issues that micro-expression sub-pixel deformations are easily overwhelmed by global noise, standard Transformers suffer from attention dilution under small-sample conditions, and deep features tend to become homogenized, this study proposes a hierarchical Transformer-based micro-expression recognition method integrating dynamic token pruning and hierarchical feature reconstruction. In the preprocessing stage, a motion magnification and Region-based Cosine-weighted Fusion strategy was introduced, employing cosine similarity as a gating coefficient to adaptively suppress the weights of noise regions irrelevant to facial expressions. In the feature encoding stage, a three-layer heterogeneous Transformer architecture was designed, sequentially embedding a dynamic token pruning module to discard low-quality tokens, a mild compression strategy to model cross-regional muscle synergies, and a hierarchical feature recombination module to break deep-layer homogenization. In the aggregation stage, a convolutional block attention module was adopted to calibrate and enhance discriminative motion responses. On the composite MEGC 2019 dataset, the proposed method achieves UF1 and UAR scores of 89.10% and 88.73%, respectively. On the CASME II, SAMM, and SMIC subsets, it attains optimal UF1 scores of 96.16%, 85.26%, and 83.39%, demonstrating the effectiveness of the proposed architecture.

Key words: Micro-expression recognition, Optical flow partitioning, Hierarchical Transformer, Deep learning, Feature recombination