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README.md

Synthetic Fingerprint Generation

In this project, we create synthetic fingerprints!

The code consist of two parts, first we create some low-resolution fingerprints using a GAN and then we train another network to make them high quality!

Data prepration

First, we need to do some pre-processing over the dataset. The images in the Sd09 fingerprints dataset are not aligned in center and have some noisy artifacts surronding the fingerprint. We need to crop the fingerprint part and clean our dataset.

After putting the dataset in the correct path and installing the NBIS tools to have nfseq bash command, you can easily modify the fingerprint-generator/crop_images_nfseg.py to create the clean dataset.

Training the fingerprint generator

After finishing the dataset pre-processing, you can easily train the fingerprint generator GAN using the fingerprint-generator/main.py code. You just need to pass the correct path to it. The code can get several parameters, which you can change to alter the training process. Here is an example of how you can use the command.

python main.py --dataset fingerprints --dataroot path/to/sd09 --imageSize 64 --nc 1 --niter 100 —cuda

Look at the main.py code to see different parameters that you can pass to the script.

After training a generator, in order to generate as many low-quality fingerprints as you want, you need to use the fingerprint-generator/generate.py code. Here is an example of how you can use this command. generator_config.json is a file created by the training code which has generator configuration. netG_epoch_X.pth is the trained model where X shows the epoch number. The generator_config.json and netG_epcoh_X.pth is by default created under the samples folder (if you didn't path the experiment argument to the training code). Using --output_dir argument, you can define the path where the generated samples will be saved and the --nimages argument specifies the number of samples to be generated.

python generate.py --config generator_config.json --weights netG_epoch_X.pth --output_dir generated_samples/ --nimages 8192 --cuda

Training the super resulotion network

The output of our generator is low quality fingerprints. We are going to use a super-resolution network to make them better. In order to do so, we need to train a network that make high resolution images from low resolution ones. For that, we need to create a low to high resolution dataset, which can be done by fingerprint-generator/create_HR_LR_images.py. You just need to set the path to Sd09 dataset in it correctly.

Then, you can use low-to-high-resolution/codes/train.py to train super-resolution network. This code needs some option file to use for trianing. You just need to set the correct paths in low-to-high-resolution/codes/options/train/train_sr.json file. Then, you can train the network via below command:

python train.py -opt options/train/train_sr.json

After training, in order to run the trained model over our generated low-quality fingerprints to make them high-resolution, you just need to use the low-to-high-resolution/codes/test.py. Similar to train, it needs some options which can be found in low-to-high-resolution/codes/options/test/test_sr.json. Do not forget to set path correctly there. Here is the test command:

python test.py -opt options/test/test_sr.json

Testing the outputs

In order to test the results, there is a extract_minutiae.py script that can be used to extract minutiae of real images and fake ones. You just need to set pathes correctly in that. Then, you can train a classifier to assess quality of my fake images. If the classifier can distinguish fake images from real ones, the quality is not good. Code for a simple classifier is in the Minutiae classifier.ipynb. You need to have NBIS tools installed to extract minutiaes.

Citation

@article{riazi2020synfi, title={SynFi: Automatic Synthetic Fingerprint Generation}, author={Riazi, M Sadegh and Chavoshian, Seyed M and Koushanfar, Farinaz}, journal={arXiv preprint arXiv:2002.08900}, year={2020} }

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