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

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

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

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.

Requirements

In order to run this you need following:

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

Usage

You can either run this on google colab or run it on your local system

• Install the pre-requisites
• Download the models (if any link fails in the notebook due to google drive restriction, try downloading them manually)
• Execute the rest of the notebook

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},
}