# VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition

arXiv: https://arxiv.org/abs/1803.07100 · HTML: https://mlsanity.com/paper/1803.07100/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation](https://mlsanity.com/paper/2205.01950/) (2022)
- [Anonymizing medical case-based explanations through disentanglement](https://mlsanity.com/paper/2311.04833/) (2023)
- [Towards Privacy-preserving Explanations in Medical Image Analysis](https://mlsanity.com/paper/2107.09652/) (2021)
- [Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation](https://mlsanity.com/paper/2412.00277/) (2024)
- [Open Set Recognition for Endoscopic Image Classification: A Deep Learning Approach on the Kvasir Dataset](https://mlsanity.com/paper/2506.18284/) (2025)
- [Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition](https://mlsanity.com/paper/2304.02309/) (2023)
- [Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition](https://mlsanity.com/paper/2305.11367/) (2023)
- [Forbes: Face Obfuscation Rendering via Backpropagation Refinement Scheme](https://mlsanity.com/paper/2407.14170/) (2024)
- [Knowledge Priors for Identity-Disentangled Open-Set Privacy-Preserving Video FER](https://mlsanity.com/paper/2603.21387/) (2026)

## Abstract (arXiv)

Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expression-equivalent face image synthesis. We evaluate the proposed model on two public datasets under various threat scenarios. Quantitative and qualitative results demonstrate that our approach strikes a balance between the preservation of privacy and data utility. We further demonstrate that our model can be effectively applied to other tasks such as expression morphing and image completion.