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

arXiv: https://arxiv.org/abs/2206.04975 · HTML: https://mlsanity.com/paper/2206.04975/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 69 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild](https://mlsanity.com/paper/2008.05924/) (2020)
- [Context-Aware Emotion Recognition Networks](https://mlsanity.com/paper/1908.05913/) (2019)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://mlsanity.com/paper/2104.00232/) (2021)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues](https://mlsanity.com/paper/1911.05659/) (2019)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation](https://mlsanity.com/paper/1906.06196/) (2019)
- [MVT: Mask Vision Transformer for Facial Expression Recognition in the wild](https://mlsanity.com/paper/2106.04520/) (2021)
- [Video-based Facial Expression Recognition using Graph Convolutional Networks](https://mlsanity.com/paper/2010.13386/) (2020)
- [Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition](https://mlsanity.com/paper/2008.02655/) (2020)
- [Emotion Recognition with Spatial Attention and Temporal Softmax Pooling](https://mlsanity.com/paper/1910.01254/) (2019)
- [Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild](https://mlsanity.com/paper/1906.02728/) (2019)

## Built on by (most cited first, 27 of 27)

- [Prompting Visual-Language Models for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2308.13382/) (2023)
- [MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2307.02227/) (2023)
- [Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2208.10335/) (2022)
- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition](https://mlsanity.com/paper/2310.16640/) (2023)
- [HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition](https://mlsanity.com/paper/2401.05698/) (2024)
- [CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition](https://mlsanity.com/paper/2303.00193/) (2023)
- [SVFAP: Self-supervised Video Facial Affect Perceiver](https://mlsanity.com/paper/2401.00416/) (2023)
- [Affective Behaviour Analysis via Integrating Multi-Modal Knowledge](https://mlsanity.com/paper/2403.10825/) (2024)
- [Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer](https://mlsanity.com/paper/2405.19100/) (2024)
- [A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP](https://mlsanity.com/paper/2403.04294/) (2024)
- [Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data](https://mlsanity.com/paper/2409.06154/) (2024)
- [OUS: Scene-Guided Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2405.18769/) (2024)
- [Robust Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2502.16129/) (2025)
- [D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition](https://mlsanity.com/paper/2406.16473/) (2024)
- [MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2404.08433/) (2024)
- [Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild](https://mlsanity.com/paper/2412.13168/) (2024)
- [Robust Low-Rank Sparse Framework for Video-Based Affective Computing](https://mlsanity.com/paper/2511.11406/) (2025)
- [Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling](https://mlsanity.com/paper/2603.11971/) (2026)
- [From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition](https://mlsanity.com/paper/2507.11892/) (2025)
- [MICACL: Multi-Instance Category-Aware Contrastive Learning for Long-Tailed Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2509.04344/) (2025)
- [Text-guided Weakly Supervised Framework for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2511.10958/) (2025)
- [Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets](https://mlsanity.com/paper/2604.10541/) (2026)
- [Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling](https://mlsanity.com/paper/2604.12777/) (2026)
- [Beyond Universality: The GCC-FER Dataset and Culture-Aware Adaptation for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2606.07063/) (2026)
- [Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions](https://mlsanity.com/paper/2606.08612/) (2026)
- [Reweighting Framewise Attention in Video Transformers for Facial Expression Understanding](https://mlsanity.com/paper/2606.30611/) (2026)

## Abstract (arXiv)

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.