NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition

TL;DR

This paper introduces NR-DFERNet, a noise-robust network for dynamic facial expression recognition in the wild, effectively filtering noisy frames and improving accuracy on challenging benchmarks.

Contribution

The paper proposes a novel noise-robust DFER network with a dynamic-static fusion module, a dynamic class token, and a snippet-based filter, addressing noise interference in wild videos.

Findings

Outperforms state-of-the-art on DFEW benchmark

Effective noise reduction in noisy video frames

Improved accuracy in wild facial expression recognition

Abstract

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.