AMSL Synthetic Fingerprint
Datasets

This license agreement covers three AMSL synthetic fingerprint datasets:

  1. AMSL SynFP SGR v1 - 50000 random fingerprints generated with a StyleGAN2-ada model from the seeds from 1 to 50000 with the truncation value 0.5. The StyleGAN2-ada model is trained from scratch with the built-in augmentation based on 408 samples from the Neurotechnology dataset collected with the Cross Match Verifer 300 sensor (https://www.neurotechnology.com/download.html). All samples are padded to 512x512 pixels prior to training. The training is performed with standard hyperparameters. The 10800 kImages model snapshot picked due to the best visual results.

    Due to the low number of unique identities in the training dataset, the synthetic samples lack diversity and it is not assured that no identity leakage happens in regard to the training data. Subjectively, the visual quality of synthetic samples is very high making them almost indistinguishable from real samples.

  2. AMSL SynFP P2P v1 - 40000 fingerprints of 500 virtual subjects, 8 fingers each (thumb is excluded), 10 impressions per finger, generated using the pix2pix model which is trained based on 39k real fingerprints making use of the directed line encoding. Training is done with batch normalization for 120 epochs (60 epochs with the constant learning rate and 60 epochs with learning rate decay).

  3. AMSL SynFP P2P v2 - 40000 fingerprints of 500 virtual subjects, 8 fingers each (thumb is excluded), 10 impressions per finger, generated using the pix2pix model which is trained based on 39k real fingerprints making use of the pointing minutiae encoding. Training is done with instance normalization for 15 epochs.

The training dataset for the both aforementioned models is comprised of 39024 samples. The original 2168 samples are from the three datasets:

We enlarged the number of training samples by performing data augmentation incl. a horizontal flip and rotations to +/-5, +/-10, +/-15 and +/-20 degree. All samples are padded to 512x512 pixels prior to training.

Random Anguli fingerprints (https://dsl.cds.iisc.ac.in/projects/Anguli/) were used as a source of minutiae for sample generation.

By downloading any of the datasets, you agree the following license conditions:

This data set is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. No author or distributor accepts responsibility to anyone for the consequences of using it or for whether it serves any particular purpose or works at all.

The synthetic fingerprint datasets are licensed under the Creative Commons Attribution-ShareAlike 3.0 License (CC BY-SA 3.0). You are free to share, to copy, distribute and transmit the data and to remix or adapt the data under the following conditions:

If you use any of the AMSL synthetic fingerprint datasets in your paper, you must cite the following paper:

@inproceedings{Makrushin23b,
author = {Andrey Makrushin and Venkata Srinath Mannam and Jana Dittmann},
title = {Data-Driven Fingerprint Reconstruction from Minutiae Based on Real and Synthetic Training Data},
booktitle = {Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
pages = {229-237},
publisher = {{SCITEPRESS}},
year = {2023},
isbn = {978-989-758-634-7},
issn = {2184-4321}
}

If you alter, transform or build upon the data, you may distribute the resulting work only under the same, similar or a compatible license.

You are not allowed to use or distribute any of the datasets or single images for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain without written permission.

All rights not expressly granted to you are reserved by the authors.


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