Abstract
Artificial fingerprint synthesis can broaden the opportunity for researchers by generating a large number of realistic synthetic fingerprints without having to worry about legal issues or privacy concerns. This paper proposes StyleGAN2 and CycleGAN based dual generative adversarial networks (GAN) system, in which StyleGAN2 generates distinct fingerprint skeletons from preprocessed data and CycleGAN transforms these skeletons into realistic fingerprints. This model can generate high-quality 256 by 256 fingerprints that can be turned into a variety of realistic fingerprint styles. Synthesized fingerprints from this model also retain features of real fingerprints that can be used in the related search system. Experimentation of the model includes visual image quality, quantitative image quality, distinctiveness test, and human perception test. The proposed model can produce more realistic high-quality fingerprints in large quantity as compared to previously reported GAN-based systems.
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The dataset generated during the current study is available in the figshare repository, https://doi.org/10.6084/m9.figshare.16628413.v1.
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The code and models that support the findings of this study are available upon request.
References
M.M. Ali, V.H. Mahale, P. Yannawar, A. Gaikwad, Overview of fingerprint recognition system, in: Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques, Chennai, India, pp. 1334–1338 (2016). https://doi.org/10.1109/ICEEOT.2016.7754900
A.H. Ansari, Generation and storage of large synthetic fingerprint database. ME Thesis, (2011) https://dsl.cds.iisc.ac.in/thesis/afzal.pdf
M. Attia, M.H. Attia, J. Iskander, K. Saleh, D. Nahavandi, A. Abobakr, M. Hossny, S. Nahavandi, Fingerprint synthesis via latent space representation, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Bari, Italy, pp. 1855–1861 (2019). https://doi.org/10.1109/SMC.2019.8914499
P. Bontrager, A. Roy, J. Togelius, N. Memon, A. Ross, Deepmasterprints: generating masterprints for dictionary attacks via latent variable evolution, in: Proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems, California, USA, pp. 1–9 (2018). https://doi.org/10.1109/BTAS.2018.8698539
K. Cao, A. Jain, Fingerprint synthesis: evaluating fingerprint search at scale, in: Proceedings of the IEEE International Conference on Biometrics, Gold Coast, QLD, Australia, pp. 31–38 (2018). https://doi.org/10.1109/ICB2018.2018.00016
R. Cappelli, D. Maio, D. Maltoni, Synthetic fingerprint-database generation, in: Proceedings of the International Conference on Pattern Recognition, Quebec City, Canada, vol. 3, pp. 744–747 (2002). https://doi.org/10.1109/ICPR.2002.1048096
S. Chen, S. Chang, Q. Huang, J. He, H. Wang, Q. Huang, Svm-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy. PLoS ONE 9(10), e111,099 (2014). https://doi.org/10.1371/journal.pone.0111099
V. Evdokimova, M. Petrov, M. Klyueva, N. Firsov, S. Bibikov, R. Skidanov, S. Popov, A. Nikonorov, Study of gan-based image reconstruction for diffractive optical systems, in: Proceedings of the International Conference on Information Technology and Nanotechnology, pp. 1–4 (2020). https://doi.org/10.1109/ITNT49337.2020.9253168
M.A. Fahim, H.Y. Jung, A lightweight GAN network for large scale fingerprint generation. IEEE Access 8, 92918–92928 (2020). https://doi.org/10.1109/ACCESS.2020.2994371
C. Gottschlich, S. Huckemann, Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints. IET Biom. 3(4), 291–301 (2014)
L. Hong, Y. Wan, A. Jain, Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998). https://doi.org/10.1109/34.709565
P. Isola, J.Y. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp. 1125–1134 (2017). https://doi.org/10.1109/CVPR.2017.632
A.K. Jain, A. Ross, S. Pankanti, Biometrics: a tool for information security. IEEE Trans. Inf. Forensics Secur. 1(2), 125–143 (2006). https://doi.org/10.1109/TIFS.2006.873653
T. Karras, S. Laine, T. Aila, A style-based generator architecture for generative adversarial networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 4401–4410 (2019). https://doi.org/10.1109/CVPR.2019.00453
T. Karras, S. Laine, M. Aittala J., Hellsten, J. Lehtinen, T. Aila, Analyzing and improving the image quality of stylegan, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, pp. 8110–8119 (2020). https://doi.org/10.1109/CVPR42600.2020.00813
X. Li, Z. Du, Y. Huang, Z. Tan, A deep translation (GAN) based change detection network for optical and sar remote sensing images. J. Photogramm. Remote Sens. 179, 14–34 (2021). https://doi.org/10.1016/j.isprsjprs.2021.07.007
C. Lin, A. Kumar, Matching contactless and contact-based conventional fingerprint images for biometrics identification. IEEE Trans. Image Process. 27(4), 2008–2021 (2018). https://doi.org/10.1109/TIP.2017.2788866
D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition (Springer, Berlin, 2009)
S. Minaee, A. Abdolrashidi, Finger-GAN: generating realistic fingerprint images using connectivity imposed gan (2018). arXiv preprint arXiv:1812.10482
Neurotechnology, VeriFinger SDK, VeriFinger fingerprint recognition technology, algorithm and SDK for PC, smartphones and Web (2022) https://www.neurotechnology.com/verifinger.html Accessed June 10, (2022)
Novetta Biosynthetic Software (2014) https://www.novetta.com/wp-content/uploads/2014/11/NOVBiosyntheticsOverview2.pdf
E. Tabassi, Nfiq 2.0: Nist fingerprint image quality. NISTIR 8034 (2016)
The Forensic Use of Bioinformation: Ethical Issues Jahrbuch für Wissenschaft und Ethik 13(1):419–430. (2008) https://doi.org/10.1515/9783110196832.3.419
Z. Wang, E.P. Simoncelli, A.C. Bovik, Multiscale structural similarity for image quality assessment, in: Proceedings of the Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, pp. 1398–1402 (2003). https://doi.org/10.1109/ACSSC.2003.1292216
A.V. Wyzykowski, M. Segundo, R. de Paula Lemes, Level three synthetic fingerprint generation, in: Proceedings of the International Conference on Pattern Recognition, Milan, Italy, pp. 9250–9257 (2021). https://doi.org/10.1109/ICPR48806.2021.9412304
Q. Zhao, L. Zhang, D. Zhang, N. Luo, Direct pore matching for fingerprint recognition, in: Proceedings of the International conference on Biometrics, Alghero, Italy, pp. 597–606 (2009). https://doi.org/10.1007/978-3-642-01793-3_61
Q. Zhao, A.K. Jain, N.G. Paulter, M. Taylor, Fingerprint image synthesis based on statistical feature models, in: Proceedings of the IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, Arlington, Virginia, USA, pp. 23–30 (2012). https://doi.org/10.1109/BTAS.2012.6374554
J.Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2223–2232 (2017). https://doi.org/10.1109/ICCV.2017.244
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Sams, A., Shomee, H.H. & Rahman, S.M.M. HQ-finGAN: High-Quality Synthetic Fingerprint Generation Using GANs. Circuits Syst Signal Process 41, 6354–6369 (2022). https://doi.org/10.1007/s00034-022-02089-1
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DOI: https://doi.org/10.1007/s00034-022-02089-1