# Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction

arXiv: https://arxiv.org/abs/2012.13217 · HTML: https://mlsanity.com/paper/2012.13217/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras](https://mlsanity.com/paper/2304.10211/) (2023)
- [Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2311.03478/) (2023)
- [eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View Synthesis](https://mlsanity.com/paper/2404.09940/) (2024)
- [SnatchML: Hijacking ML models without Training Access](https://mlsanity.com/paper/2406.01708/) (2024)
- [Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression Recognition](https://mlsanity.com/paper/2411.13024/) (2024)
- [From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition](https://mlsanity.com/paper/2507.11892/) (2025)
- [LP2DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition](https://mlsanity.com/paper/2604.15707/) (2026)

## Abstract (arXiv)

Video facial expression recognition is useful for many applications and received much interest lately. Although some solutions give really good results in a controlled environment (no occlusion), recognition in the presence of partial facial occlusion remains a challenging task. To handle occlusions, solutions based on the reconstruction of the occluded part of the face have been proposed. These solutions are mainly based on the texture or the geometry of the face. However, the similarity of the face movement between different persons doing the same expression seems to be a real asset for the reconstruction. In this paper we exploit this asset and propose a new solution based on an auto-encoder with skip connections to reconstruct the occluded part of the face in the optical flow domain. To the best of our knowledge, this is the first proposition to directly reconstruct the movement for facial expression recognition. We validated our approach in the controlled dataset CK+ on which different occlusions were generated. Our experiments show that the proposed method reduce significantly the gap, in terms of recognition accuracy, between occluded and non-occluded situations. We also compare our approach with existing state-of-the-art solutions. In order to lay the basis of a reproducible and fair comparison in the future, we also propose a new experimental protocol that includes occlusion generation and reconstruction evaluation.