# MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias

arXiv: https://arxiv.org/abs/2012.05225 · HTML: https://mlsanity.com/paper/2012.05225/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Controllable and Guided Face Synthesis for Unconstrained Face Recognition](https://mlsanity.com/paper/2207.10180/) (2022)
- [FaceLit: Neural 3D Relightable Faces](https://mlsanity.com/paper/2303.15437/) (2023)
- [Bi-LORA: A Vision-Language Approach for Synthetic Image Detection](https://mlsanity.com/paper/2404.01959/) (2024)
- [Detecting Images Generated by Diffusers](https://mlsanity.com/paper/2303.05275/) (2023)
- [Simulated Adversarial Testing of Face Recognition Models](https://mlsanity.com/paper/2106.04569/) (2021)
- [PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation](https://mlsanity.com/paper/2303.07337/) (2023)
- [Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing](https://mlsanity.com/paper/2211.16499/) (2022)
- [3D human tongue reconstruction from single "in-the-wild" images](https://mlsanity.com/paper/2106.12302/) (2021)
- [Human Body Measurement Estimation with Adversarial Augmentation](https://mlsanity.com/paper/2210.05667/) (2022)
- [ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation](https://mlsanity.com/paper/2311.01929/) (2023)
- [DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis](https://mlsanity.com/paper/2302.14533/) (2023)
- [Are Pose Estimators Ready for the Open World? STAGE: A GenAI Toolkit for Auditing 3D Human Pose Estimators](https://mlsanity.com/paper/2408.16536/) (2024)
- [Sampling Strategies for Mitigating Bias in Face Synthesis Methods](https://mlsanity.com/paper/2405.11320/) (2024)
- [Inclusive Review on Advances in Masked Human Face Recognition Technologies](https://mlsanity.com/paper/2508.00841/) (2025)

## Abstract (arXiv)

To detect bias in face recognition networks, it can be useful to probe a network under test using samples in which only specific attributes vary in some controlled way. However, capturing a sufficiently large dataset with specific control over the attributes of interest is difficult. In this work, we describe a simulator that applies specific head pose and facial expression adjustments to images of previously unseen people. The simulator first fits a 3D morphable model to a provided image, applies the desired head pose and facial expression controls, then renders the model into an image. Next, a conditional Generative Adversarial Network (GAN) conditioned on the original image and the rendered morphable model is used to produce the image of the original person with the new facial expression and head pose. We call this conditional GAN -- MorphGAN. Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression. Images generated by MorphGAN can also serve as data augmentation when training data are scarce. We show that by augmenting small datasets of faces with new poses and expressions improves the recognition performance by up to 9% depending on the augmentation and data scarcity.