# Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

arXiv: https://arxiv.org/abs/2201.09061 · HTML: https://mlsanity.com/paper/2201.09061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Facial Emotion Recognition: State of the Art Performance on FER2013](https://mlsanity.com/paper/2105.03588/) (2021)
- [Facial Expression Recognition Using Disentangled Adversarial Learning](https://mlsanity.com/paper/1909.13135/) (2019)

## Built on by (most cited first, 2 of 2)

- [FExGAN-Meta: Facial Expression Generation with Meta Humans](https://mlsanity.com/paper/2203.05975/) (2022)
- [Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description](https://mlsanity.com/paper/2410.02049/) (2024)

## Abstract (arXiv)

Facial expressions are a form of non-verbal communication that humans perform seamlessly for meaningful transfer of information. Most of the literature addresses the facial expression recognition aspect however, with the advent of Generative Models, it has become possible to explore the affect space in addition to mere classification of a set of expressions. In this article, we propose a generative model architecture which robustly generates a set of facial expressions for multiple character identities and explores the possibilities of generating complex expressions by combining the simple ones.