# Facial Motion Prior Networks for Facial Expression Recognition

arXiv: https://arxiv.org/abs/1902.08788 · HTML: https://mlsanity.com/paper/1902.08788/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2019.

## Built on by (most cited first, 20 of 20)

- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (2021)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition](https://mlsanity.com/paper/2307.11404/) (2023)
- [Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2106.03487/) (2021)
- [Towards Unbiased Visual Emotion Recognition via Causal Intervention](https://mlsanity.com/paper/2107.12096/) (2021)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation](https://mlsanity.com/paper/2105.06421/) (2021)
- [Face Trees for Expression Recognition](https://mlsanity.com/paper/2112.02487/) (2021)
- [Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis](https://mlsanity.com/paper/2203.12570/) (2022)
- [FaceTopoNet: Facial Expression Recognition using Face Topology Learning](https://mlsanity.com/paper/2209.06322/) (2022)
- [Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition](https://mlsanity.com/paper/2303.04896/) (2023)
- [Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition](https://mlsanity.com/paper/2109.03336/) (2021)
- [Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training](https://mlsanity.com/paper/2107.05736/) (2021)
- [Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images](https://mlsanity.com/paper/2109.08915/) (2021)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)
- [Imponderous Net for Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2103.15136/) (2021)
- [xLSTM-FER: Enhancing Student Expression Recognition with Extended Vision Long Short-Term Memory Network](https://mlsanity.com/paper/2410.05074/) (2024)
- [Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations](https://mlsanity.com/paper/2107.04746/) (2021)
- [GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution](https://mlsanity.com/paper/2410.15927/) (2024)

## Abstract (arXiv)

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.