# Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition

arXiv: https://arxiv.org/abs/2005.06040 · HTML: https://mlsanity.com/paper/2005.06040/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 17 of 17)

- [Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition](https://mlsanity.com/paper/2109.07270/) (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)
- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [Proto-Former: Unified Facial Landmark Detection by Prototype Transformer](https://mlsanity.com/paper/2510.15338/) (2025)
- [Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training](https://mlsanity.com/paper/2107.05736/) (2021)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)
- [Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos](https://mlsanity.com/paper/2303.10421/) (2023)
- [Imponderous Net for Facial Expression Recognition in the Wild](https://mlsanity.com/paper/2103.15136/) (2021)
- [Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations](https://mlsanity.com/paper/2107.04746/) (2021)
- [Region attention and graph embedding network for occlusion objective class-based micro-expression recognition](https://mlsanity.com/paper/2107.05904/) (2021)
- [Masked Student Dataset of Expressions](https://mlsanity.com/paper/2304.03867/) (2023)
- [Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition](https://mlsanity.com/paper/2305.01884/) (2023)
- [HB-net: Holistic bursting cell cluster integrated network for occluded multi-objects recognition](https://mlsanity.com/paper/2310.11834/) (2023)
- [Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition](https://mlsanity.com/paper/2404.15714/) (2024)
- [GLaRE: A Graph-based Landmark Region Embedding Network for Emotion Recognition](https://mlsanity.com/paper/2508.20579/) (2025)
- [FAHCD-Net: Frequency-Adaptive Heatmap-Conditional Diffusion Networks for Robust Facial Landmark Detection](https://mlsanity.com/paper/2609.16842/) (2026)

## Abstract (arXiv)

Recognizing the expressions of partially occluded faces is a challenging computer vision problem. Previous expression recognition methods, either overlooked this issue or resolved it using extreme assumptions. Motivated by the fact that the human visual system is adept at ignoring the occlusion and focus on non-occluded facial areas, we propose a landmark-guided attention branch to find and discard corrupted features from occluded regions so that they are not used for recognition. An attention map is first generated to indicate if a specific facial part is occluded and guide our model to attend to non-occluded regions. To further improve robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and task each block to predict the expression independently. This results in more diverse and discriminative features, enabling the expression recognition system to recover even though the face is partially occluded. Depending on the synergistic effects of the two branches, our occlusion-adaptive deep network significantly outperforms state-of-the-art methods on two challenging in-the-wild benchmark datasets and three real-world occluded expression datasets.