  Dynamic Facial Expression Recognition (DFER) is crucial for affective
computing but often overlooks the impact of scene context. We have identified a
significant issue in current DFER tasks: human annotators typically integrate
emotions from various angles, including environmental cues and body language,
whereas existing DFER methods tend to consider the scene as noise that needs to
be filtered out, focusing solely on facial information. We refer to this as the
Rigid Cognitive Problem. The Rigid Cognitive Problem can lead to discrepancies
between the cognition of annotators and models in some samples. To align more
closely with the human cognitive paradigm of emotions, we propose an Overall
Understanding of the Scene DFER method (OUS). OUS effectively integrates scene
and facial features, combining scene-specific emotional knowledge for DFER.
Extensive experiments on the two largest datasets in the DFER field, DFEW and
FERV39k, demonstrate that OUS significantly outperforms existing methods. By
analyzing the Rigid Cognitive Problem, OUS successfully understands the complex
relationship between scene context and emotional expression, closely aligning
with human emotional understanding in real-world scenarios.
