FRD-BFAN: feature region delineation and bilateral feature attention networks for facial expression recognition in the wild

Facial expression recognition (FER) in unconstrained scenarios remains difficult due to diverse facial variations and background disturbances. In this work, we propose FRD-BFAN, a feature-region delineation and bilateral feature attention network for robust in-the-wild FER. FRD-BFAN adopts a hierarchical region modeling scheme implemented by a three-branch architecture, where a global branch captures holistic semantics, while local–global and local–local branches progressively refine discriminative regional cues through coarse-to-fine partitioning. To further enhance informative responses and suppress irrelevant patterns, we introduce a bilateral feature attention module that jointly recalibrates features along spatial and channel dimensions, and employ a weighted fusion with multi-branch supervision to stabilize optimization. Experiments on multiple in-the-wild benchmarks demonstrate that FRD-BFAN consistently improves recognition performance and robustness over recent methods, validating the effectiveness of the proposed region delineation and bilateral attention design.