# Uncertainty-aware Label Distribution Learning for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2209.10448 · HTML: https://mlsanity.com/paper/2209.10448/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Probabilistic End-to-end Noise Correction for Learning with Noisy Labels](https://mlsanity.com/paper/1903.07788/) (2019)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [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)
- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://mlsanity.com/paper/1710.03144/) (2017)
- [MVT: Mask Vision Transformer for Facial Expression Recognition in the wild](https://mlsanity.com/paper/2106.04520/) (2021)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Auxiliary Image Regularization for Deep CNNs with Noisy Labels](https://mlsanity.com/paper/1511.07069/) (2015)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [Fine-Grained Visual Classification using Self Assessment Classifier](https://mlsanity.com/paper/2205.10529/) (2022)
- [CAKE: Compact and Accurate K-dimensional representation of Emotion](https://mlsanity.com/paper/1807.11215/) (2018)

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

- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [Music-Driven Group Choreography](https://mlsanity.com/paper/2303.12337/) (2023)
- [Exploiting Multi-Label Correlation in Label Distribution Learning](https://mlsanity.com/paper/2308.01742/) (2023)
- [LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise](https://mlsanity.com/paper/2307.09023/) (2023)
- [Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition](https://mlsanity.com/paper/2310.19636/) (2023)
- [Uncertain Facial Expression Recognition via Multi-task Assisted Correction](https://mlsanity.com/paper/2212.07144/) (2022)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data](https://mlsanity.com/paper/2410.09865/) (2024)
- [Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer](https://mlsanity.com/paper/2404.17205/) (2024)
- [Style Transfer for 2D Talking Head Animation](https://mlsanity.com/paper/2303.09799/) (2023)
- [Conformal Depression Prediction](https://mlsanity.com/paper/2405.18723/) (2024)
- [Interpretable Uncertainty Routing Separating Emotion Ambiguity from Distribution Shift in Facial Expression Recognition](https://mlsanity.com/paper/2606.22725/) (2026)
- [Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition](https://mlsanity.com/paper/2401.11123/) (2024)
- [Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation](https://mlsanity.com/paper/2506.20867/) (2025)
- [Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity](https://mlsanity.com/paper/2605.04827/) (2026)
- [The Circumplex Degeneracy Behind the Rare-Class Limit in Affect Recognition](https://mlsanity.com/paper/2606.15763/) (2026)
- [TabMixer: Excavating Label Distribution Learning with Small-scale Features](https://mlsanity.com/paper/2210.13852/) (2022)
- [Cross-domain Robust Deepfake Bias Expansion Network for Face Forgery Detection](https://mlsanity.com/paper/2310.05124/) (2023)
- [Be Bayesian by Attachments to Catch More Uncertainty](https://mlsanity.com/paper/2310.13027/) (2023)
- [Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning](https://mlsanity.com/paper/2405.20600/) (2024)
- [GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution](https://mlsanity.com/paper/2410.15927/) (2024)
- [Latent label distribution grid representation for modeling uncertainty](https://mlsanity.com/paper/2505.21180/) (2025)
- [EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning](https://mlsanity.com/paper/2511.20106/) (2025)
- [Enhance and Reuse: A Dual-Mechanism Approach to Boost Deep Forest for Label Distribution Learning](https://mlsanity.com/paper/2602.06353/) (2026)
- [Revisiting Emotions Representation for Recognition in the Wild](https://mlsanity.com/paper/2602.06778/) (2026)
- [Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring](https://mlsanity.com/paper/2603.16719/) (2026)
- [On Applicability of Synthetic Datasets for Facial Expression Recognition](https://mlsanity.com/paper/2605.17483/) (2026)
- [Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions](https://mlsanity.com/paper/2607.17121/) (2026)
- [Emotion as a Distribution: Joint Valence-Arousal Probability Learning for Speaker-Independent Multimodal Emotion Recognition](https://mlsanity.com/paper/2609.05755/) (2026)
- [Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2609.17130/) (2026)

## Abstract (arXiv)

Despite significant progress over the past few years, ambiguity is still a key challenge in Facial Expression Recognition (FER). It can lead to noisy and inconsistent annotation, which hinders the performance of deep learning models in real-world scenarios. In this paper, we propose a new uncertainty-aware label distribution learning method to improve the robustness of deep models against uncertainty and ambiguity. We leverage neighborhood information in the valence-arousal space to adaptively construct emotion distributions for training samples. We also consider the uncertainty of provided labels when incorporating them into the label distributions. Our method can be easily integrated into a deep network to obtain more training supervision and improve recognition accuracy. Intensive experiments on several datasets under various noisy and ambiguous settings show that our method achieves competitive results and outperforms recent state-of-the-art approaches. Our code and models are available at https://github.com/minhnhatvt/label-distribution-learning-fer-tf.