# Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition

arXiv: https://arxiv.org/abs/2104.05160 · HTML: https://mlsanity.com/paper/2104.05160/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [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)
- [Person Re-identification with Deep Similarity-Guided Graph Neural Network](https://mlsanity.com/paper/1807.09975/) (2018)
- [Covariance Pooling For Facial Expression Recognition](https://mlsanity.com/paper/1805.04855/) (2018)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://mlsanity.com/paper/1710.03144/) (2017)
- [FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition](https://mlsanity.com/paper/1609.06591/) (2016)
- [Peak-Piloted Deep Network for Facial Expression Recognition](https://mlsanity.com/paper/1607.06997/) (2016)

## Built on by (most cited first, 30 of 49)

- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [POSTER++: A simpler and stronger facial expression recognition network](https://mlsanity.com/paper/2301.12149/) (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)
- [Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding](https://mlsanity.com/paper/2304.00058/) (2023)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [ReSup: Reliable Label Noise Suppression for Facial Expression Recognition](https://mlsanity.com/paper/2305.17895/) (2023)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Towards Unbiased Visual Emotion Recognition via Causal Intervention](https://mlsanity.com/paper/2107.12096/) (2021)
- [Disentangling Identity and Pose for Facial Expression Recognition](https://mlsanity.com/paper/2208.08106/) (2022)
- [Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers](https://mlsanity.com/paper/2207.11081/) (2022)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data](https://mlsanity.com/paper/2503.20771/) (2025)
- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [Generalizable Facial Expression Recognition](https://mlsanity.com/paper/2408.10614/) (2024)
- [Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition](https://mlsanity.com/paper/2207.07973/) (2022)
- [Privileged Attribution Constrained Deep Networks for Facial Expression Recognition](https://mlsanity.com/paper/2203.12905/) (2022)
- [FaceTopoNet: Facial Expression Recognition using Face Topology Learning](https://mlsanity.com/paper/2209.06322/) (2022)
- [AU-Aware Vision Transformers for Biased Facial Expression Recognition](https://mlsanity.com/paper/2211.06609/) (2022)
- [Unsupervised Facial Expression Representation Learning with Contrastive Local Warping](https://mlsanity.com/paper/2303.09034/) (2023)
- [In the Blink of an Eye: Event-based Emotion Recognition](https://mlsanity.com/paper/2310.04043/) (2023)
- [Rethinking the Learning Paradigm for Facial Expression Recognition](https://mlsanity.com/paper/2209.15402/) (2022)
- [Open-Set Facial Expression Recognition](https://mlsanity.com/paper/2401.12507/) (2024)
- [Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition](https://mlsanity.com/paper/2407.09521/) (2024)
- [Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation](https://mlsanity.com/paper/2409.08598/) (2024)
- [DIG-FACE: De-biased Learning for Generalized Facial Expression Category Discovery](https://mlsanity.com/paper/2409.20098/) (2024)
- [QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition](https://mlsanity.com/paper/2411.01988/) (2024)
- [Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation](https://mlsanity.com/paper/2106.04332/) (2021)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRL mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (FRN). In particular, FDN first decomposes the basic features extracted from a backbone network into a set of facial action-aware latent features to model expression similarities. Then, FRN captures the intra-feature and inter-feature relationships for latent features to characterize expression-specific variations, and reconstructs the expression feature. To this end, two modules including an intra-feature relation modeling module and an inter-feature relation modeling module are developed in FRN. Experimental results on both the in-the-lab databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods. This clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.