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README.md

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+# 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
+
+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},
+}
+
+```