# Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing

arXiv: https://arxiv.org/abs/2007.04514 · HTML: https://mlsanity.com/paper/2007.04514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2401.11085/) (2024)
- [Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis](https://mlsanity.com/paper/2409.19582/) (2024)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [A Backbone Benchmarking Study on Self-supervised Learning as a Auxiliary Task with Texture-based Local Descriptors for Face Analysis](https://mlsanity.com/paper/2603.22190/) (2026)
- [A Multi-Task Deep Learning Framework for Pose-Invariant Face Recognition and Facial Landmark Localization](https://mlsanity.com/paper/2610.16849/) (2026)

## Abstract (arXiv)

Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.