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--- |
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language: en |
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tags: |
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- Computer Vision |
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- Machine Learning |
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- Deep Learning |
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--- |
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# Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network |
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This is the implementation of the FExGAN proposed in the following article: |
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[Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network](https://www.arxiv.com) |
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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. |
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# Requirements |
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In order to run this you need following: |
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* Python >= 3.7 |
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* Tensorflow >= 2.6 |
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* CUDA enabled GPU (e.g. GTX1070/GTX1080) |
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# Usage Code |
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https://www.github.com/azadlab/FExGAN |
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# Citation |
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If you use any part of this code or use ideas mentioned in the paper, please cite the following article. |
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``` |
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@article{Siddiqui_FExGAN_2022, |
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author = {{Siddiqui}, J. Rafid}, |
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title = {{Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network}}, |
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journal = {ArXiv e-prints}, |
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archivePrefix = "arXiv", |
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keywords = {Deep Learning, GAN, Facial Expressions}, |
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year = {2022} |
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url = {http://arxiv.org/abs/2201.09061}, |
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} |
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``` |
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