  Dynamic facial expression recognition (DFER) in the wild is an extremely
challenging task, due to a large number of noisy frames in the video sequences.
Previous works focus on extracting more discriminative features, but ignore
distinguishing the key frames from the noisy frames. To tackle this problem, we
propose a noise-robust dynamic facial expression recognition network
(NR-DFERNet), which can effectively reduce the interference of noisy frames on
the DFER task. Specifically, at the spatial stage, we devise a dynamic-static
fusion module (DSF) that introduces dynamic features to static features for
learning more discriminative spatial features. To suppress the impact of target
irrelevant frames, we introduce a novel dynamic class token (DCT) for the
transformer at the temporal stage. Moreover, we design a snippet-based filter
(SF) at the decision stage to reduce the effect of too many neutral frames on
non-neutral sequence classification. Extensive experimental results demonstrate
that our NR-DFERNet outperforms the state-of-the-art methods on both the DFEW
and AFEW benchmarks.
