  This paper introduces LLDif, a novel diffusion-based facial expression
recognition (FER) framework tailored for extremely low-light (LL) environments.
Images captured under such conditions often suffer from low brightness and
significantly reduced contrast, presenting challenges to conventional methods.
These challenges include poor image quality that can significantly reduce the
accuracy of emotion recognition. LLDif addresses these issues with a novel
two-stage training process that combines a Label-aware CLIP (LA-CLIP), an
embedding prior network (PNET), and a transformer-based network adept at
handling the noise of low-light images. The first stage involves LA-CLIP
generating a joint embedding prior distribution (EPD) to guide the LLformer in
label recovery. In the second stage, the diffusion model (DM) refines the EPD
inference, ultilising the compactness of EPD for precise predictions.
Experimental evaluations on various LL-FER datasets have shown that LLDif
achieves competitive performance, underscoring its potential to enhance FER
applications in challenging lighting conditions.
