A dual uncertainty-aware fusion framework for face expression recognition in the wild

Facial Expression Recognition(FER) is a key task in the broader landscape of affective computing and human-computer interaction, enabling machines to interpret human emotions. To better learn discriminative features under complex facial variations, recent FER research has increasingly adopted multi-branch fusion architectures that aim to capture complementary features from diverse perspectives. However, existing multi-branch fusion strategies, including static weighting, simple concatenation, or uncertainty-aware modeling, lack the capacity to comprehensively capture and reconcile the reliability variations across both individual instances and structural branches. To overcome these limitations, we propose a novel multi-branch fusion strategy, named Dual Uncertainty-Aware Fusion Framework(DUAFF), which improves the discriminability of integrated features by simultaneously modeling instance-wise uncertainty and inter-branch correlations. Specifically, the proposed method comprises two complementary modules: Instance-Discrepant Uncertainty-Aware Fusion Module (ID-UAFM) and Branch-Discrepant Uncertainty-Aware Fusion Module (BD-UAFM). ID-UAFM is introduced to perform channel-wise entropy analysis between semantically distinct samples to estimate instance-level uncertainty, enabling selective channel-wise fusion that emphasizes reliable representations while suppressing uncertain responses. BD-UAFM is further proposed to capture structural uncertainty by evaluating the relative reliability of features across multiple branches and adaptively weighting their contributions based on inter-branch discrepancies. Experimental results demonstrate that the proposed DUAFF consistently outperforms POSTER across three benchmark datasets, achieving accuracy improvements of 0.23 % on RAF-DB, 0.69 % on FER2013, and 0.29 % on AffectNet (7-class), thereby confirming its effectiveness in enhancing the reliability and discriminability of facial representations.