---
language: en
tags:
- Computer Vision
- Machine Learning
- Deep Learning
---

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

![FExGAN GIF Demo](https://github.com/azadlab/FExGAN/blob/master/FExGAN.gif?raw=true)

This is the implementation of the FExGAN proposed in the following article:

[Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network](https://www.arxiv.com)

FExGAN takes input an image and a vector of desired affect (e.g. angry,disgust,sad,surprise,joy,neutral and fear) and converts the input image to the desired emotion while keeping the identity of the original image.

![FExGAN GIF Demo](https://github.com/azadlab/FExGAN/blob/master/results.png?raw=true)

# Requirements

In order to run this you need following:

* Python >= 3.7
* Tensorflow >= 2.6
* CUDA enabled GPU (e.g. GTX1070/GTX1080)


# Usage Code

https://www.github.com/azadlab/FExGAN

# Citation

If you use any part of this code or use ideas mentioned in the paper, please cite the following article.

```
@article{Siddiqui_FExGAN_2022,
  author = {{Siddiqui}, J. Rafid},
  title = {{Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network}},
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv",
  keywords = {Deep Learning, GAN, Facial Expressions},
  year = {2022}
  url = {http://arxiv.org/abs/2201.09061},
}

```