# Suppressing Uncertainties for Large-Scale Facial Expression Recognition

arXiv: https://arxiv.org/abs/2002.10392 · HTML: https://mlsanity.com/paper/2002.10392/

## Key facts

- Cited by 157 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 30 of 157)

- [Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning](https://mlsanity.com/paper/2406.11161/) (2024)
- [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)
- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion](https://mlsanity.com/paper/2103.16854/) (2021)
- [Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition](https://mlsanity.com/paper/2104.05160/) (2021)
- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning](https://mlsanity.com/paper/2203.03137/) (2022)
- [Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2208.10335/) (2022)
- [SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation](https://mlsanity.com/paper/2308.11509/) (2023)
- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin](https://mlsanity.com/paper/2203.12341/) (2022)
- [Learning Emotion Representations from Verbal and Nonverbal Communication](https://mlsanity.com/paper/2305.13500/) (2023)
- [Uncertainty-aware Label Distribution Learning for Facial Expression Recognition](https://mlsanity.com/paper/2209.10448/) (2022)
- [CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition](https://mlsanity.com/paper/2303.00193/) (2023)
- [EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning](https://mlsanity.com/paper/2408.11424/) (2024)
- [MVT: Mask Vision Transformer for Facial Expression Recognition in the wild](https://mlsanity.com/paper/2106.04520/) (2021)
- [NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2206.04975/) (2022)
- [POSTER++: A simpler and stronger facial expression recognition network](https://mlsanity.com/paper/2301.12149/) (2023)
- [RoI Tanh-polar Transformer Network for Face Parsing in the Wild](https://mlsanity.com/paper/2102.02717/) (2021)
- [COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition](https://mlsanity.com/paper/2206.05833/) (2022)
- [Learning to Amend Facial Expression Representation via De-albino and Affinity](https://mlsanity.com/paper/2103.10189/) (2021)
- [A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis](https://mlsanity.com/paper/2107.04225/) (2021)
- [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)
- [LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise](https://mlsanity.com/paper/2307.09023/) (2023)

## Abstract (arXiv)

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression Recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus. The code will be available at hrefhttps://github.com/kaiwang960112/Self-Cure-Networkhttps://github.com/kaiwang960112/Self-Cure-Network.