# AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition

arXiv: https://arxiv.org/abs/2205.11785 · HTML: https://mlsanity.com/paper/2205.11785/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 24 May 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation](https://mlsanity.com/paper/1905.10089/) (2019)
- [Learning from Millions of 3D Scans for Large-scale 3D Face Recognition](https://mlsanity.com/paper/1711.05942/) (2017)
- [A Real Time Facial Expression Classification System Using Local Binary Patterns](https://mlsanity.com/paper/1505.04058/) (2015)
- [Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition](https://mlsanity.com/paper/1803.05846/) (2018)
- [Expression Analysis Based on Face Regions in Read-world Conditions](https://mlsanity.com/paper/1911.05188/) (2019)
- [2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning](https://mlsanity.com/paper/2011.08333/) (2020)

## Built on by (most cited first, 3 of 3)

- [Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model](https://mlsanity.com/paper/2504.19739/) (2025)
- [DAP-MAE: Domain-Adaptive Point Cloud Masked Autoencoder for Effective Cross-Domain Learning](https://mlsanity.com/paper/2510.21635/) (2025)
- [DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition](https://mlsanity.com/paper/2403.08318/) (2024)

## Abstract (arXiv)

2D+3D facial expression recognition (FER) can effectively cope with illumination changes and pose variations by simultaneously merging 2D texture and more robust 3D depth information. Most deep learning-based approaches employ the simple fusion strategy that concatenates the multimodal features directly after fully-connected layers, without considering the different degrees of significance for each modality. Meanwhile, how to focus on both 2D and 3D local features in salient regions is still a great challenge. In this letter, we propose the adaptive fusion network with masks (AFNet-M) for 2D+3D FER. To enhance 2D and 3D local features, we take the masks annotating salient regions of the face as prior knowledge and design the mask attention module (MA) which can automatically learn two modulation vectors to adjust the feature maps. Moreover, we introduce a novel fusion strategy that can perform adaptive fusion at convolutional layers through the designed importance weights computing module (IWC). Experimental results demonstrate that our AFNet-M achieves the state-of-the-art performance on BU-3DFE and Bosphorus datasets and requires fewer parameters in comparison with other models.