Face sketch-photo transformation has broad applications in forensics, law enforcement, and digital entertainment, particular for face recognition systems that are designed for photo-to-photo matching. While there are a number of methods for face photo-to-sketch transformation, studies on sketch-to-photo transformation remain limited. The generated face photos are used, as a replacement of face sketches, and particularly for face identification against a gallery set of photos. Experimental results show that the proposed approach is able to generate realistic photos from sketches, and the generated photos are instrumental in improving the sketch identification accuracy against a large Dataset.
GANs (generative adversarial network):
Generative Adversarial Networks (GANs) are a powerful class of neural networks that are used for unsupervised learning. It was developed and introduced by Ian J. Goodfellow in 2014. GANs are basically made up of a system of two competing neural network models which compete with each other and are able to analyze, capture and copy the variations within a dataset.
Here is the full code to build the Sketch to FACE Recognition model and the Dataset that has been used from Kaggle (Pretty Face).
.ipynb file: notebook02d1e8dce6 | Kaggle
Dataset: Pretty Face | Kaggle
My whole project is available on AdityaLalwani/sketch-to-face-gans (github.com).
Create virtual environment
A virtual environment is a tool that helps to keep dependencies required by different projects separate by creating isolated python virtual environments for them. This is one of the most important tools that most of the Python developers use.
python -m venv ./venv
Clone the project in specific environment (venv)
$ git clone https://github.com/mtobeiyf/keras-flask-deploy-webapp.git
Install Required Packages
pip install -r requirements.txt
Run routes.py file
Go to http://localhost:5000