  Dynamic facial expression recognition (DFER) in the wild is still hindered by
data limitations, e.g., insufficient quantity and diversity of pose, occlusion
and illumination, as well as the inherent ambiguity of facial expressions. In
contrast, static facial expression recognition (SFER) currently shows much
higher performance and can benefit from more abundant high-quality training
data. Moreover, the appearance features and dynamic dependencies of DFER remain
largely unexplored. To tackle these challenges, we introduce a novel
Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and
dynamic information implicitly encoded in extracted facial landmark-aware
features, thereby significantly improving DFER performance. Firstly, we build
and train an image model for SFER, which incorporates a standard Vision
Transformer (ViT) and Multi-View Complementary Prompters (MCPs) only. Then, we
obtain our video model (i.e., S2D), for DFER, by inserting Temporal-Modeling
Adapters (TMAs) into the image model. MCPs enhance facial expression features
with landmark-aware features inferred by an off-the-shelf facial landmark
detector. And the TMAs capture and model the relationships of dynamic changes
in facial expressions, effectively extending the pre-trained image model for
videos. Notably, MCPs and TMAs only increase a fraction of trainable parameters
(less than +10\%) to the original image model. Moreover, we present a novel
Emotion-Anchors (i.e., reference samples for each emotion category) based
Self-Distillation Loss to reduce the detrimental influence of ambiguous emotion
labels, further enhancing our S2D. Experiments conducted on popular SFER and
DFER datasets show that we achieve the state of the art.
