naveenvenk17 / face-generation-from-sketch-using-gans

This repository contains the code for implementing an image generation system using GAN (Generative Adversarial Networks) to turn face sketches into realistic photos.

Face Sketch to Image Generation using GAN

This repository contains the code for implementing an image generation system using GAN (Generative Adversarial Networks) to turn face sketches into realistic photos.

Contents

• Getting started
• Data Augmentation
• Training
• Performance Measurement
• Testing
• References

Install requirements

pip install -r requirements.txt

Keras-contrib installation

• Run the below command to copy the source code locally (Assuming git is installed) https://www.github.com/keras-team/keras-contrib.git
• cd keras-contrib
• python setup.py install

Or you can refer to this link https://medium.com/@kegui/how-to-install-keras-contrib-7b75334ab742

Data Augmentation

• The dataset consists 100 pairs faces and sketches. 88 pairs were chosen for training set.
• Each pair is augmented to 200 pairs of face and sketch using transformation, rotation and shearing.
• Do Data Augmentation with this notebook

GAN

• The GAN model architecture involves two sub-models: a generator model for generating new examples and a discriminator model for classifying whether generated examples are real, from the domain, or fake, generated by the generator model.

• GAN model consists of Generator model with 50 layers and Adversary Model with 9 layers.

Training

• Start training GAN model with this notebook
• GAN model trained for 82 epochs
• Generator model Loss = 2.052
• Discriminator model Loss = 1.139

Model Prediction after

• 1 epoch :

• 10 epoch :

• 25 epoch :

• 50 epoch :

• 82 epoch :

Performance Measurement

• SSIM (Structural Similarity Index) is used for measuring the similarity between two images.
• SSIM = 0.7858
• L2 Norm = 93.452
• Calculate SSIM and Verification Accuracy (L2-norm) using this notebook

Testing

Generate single image with this notebook

References

[1] X. Wang and X. Tang. (2009). Face Photo-Sketch Synthesis and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 31(11), 1955-1967.

[2] W. Zhang, X. Wang and X. Tang. (2011). Coupled Information-Theoretic Encoding for Face Photo-Sketch Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3] https://github.com/Malikanhar/Face-Sketch-to-Image-Generation-using-GAN