  This study introduces LRDif, a novel diffusion-based framework designed
specifically for facial expression recognition (FER) within the context of
under-display cameras (UDC). To address the inherent challenges posed by UDC's
image degradation, such as reduced sharpness and increased noise, LRDif employs
a two-stage training strategy that integrates a condensed preliminary
extraction network (FPEN) and an agile transformer network (UDCformer) to
effectively identify emotion labels from UDC images. By harnessing the robust
distribution mapping capabilities of Diffusion Models (DMs) and the spatial
dependency modeling strength of transformers, LRDif effectively overcomes the
obstacles of noise and distortion inherent in UDC environments. Comprehensive
experiments on standard FER datasets including RAF-DB, KDEF, and FERPlus, LRDif
demonstrate state-of-the-art performance, underscoring its potential in
advancing FER applications. This work not only addresses a significant gap in
the literature by tackling the UDC challenge in FER but also sets a new
benchmark for future research in the field.
