# Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2303.11840 · HTML: https://mlsanity.com/paper/2303.11840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [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)
- [Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition](https://mlsanity.com/paper/2104.05160/) (2021)
- [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)
- [Non-Linear Fusion for Self-Paced Multi-View Clustering](https://mlsanity.com/paper/2104.09255/) (2021)
- [A comparative study on movement feature in different directions for micro-expression recognition](https://mlsanity.com/paper/2102.08068/) (2021)

## Abstract (arXiv)

The accuracy of facial expression recognition is typically affected by the following factors: high similarities across different expressions, disturbing factors, and micro-facial movement of rapid and subtle changes. One potentially viable solution for addressing these barriers is to exploit the neutral information concealed in neutral expression images. To this end, in this paper we propose a self-Paced Neutral Expression-Disentangled Learning (SPNDL) model. SPNDL disentangles neutral information from facial expressions, making it easier to extract key and deviation features. Specifically, it allows to capture discriminative information among similar expressions and perceive micro-facial movements. In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts introduced by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method.