Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31324
Generative Adversarial Network Based Fingerprint Anti-Spoofing Limitations

Authors: Yehjune Heo


Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: Fingerprint Recognition, gan, CNN, anti-spoofing

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 0


[1] T. van der Putte and J. Keuning, “Biometrical Fingerprint Recognition: Don’t Get Your Fingers Burned” in Smart Card Research and Advanced Applications, pp. 289-306, 2000.
[2] A. Hori and I. Fujieda, “Study on Blood Movement During Fingerprint Input Actions”, International Journal of Optomechatronics, Vol. 2, pp.390-400, 2008.
[3] E. Park, X. Cui, W. Kim, and Haki. Kim, “End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module”, ArXiv, 2018.
[4] E. Marasco and A. Ross, “A Survey on Antispoofing Schemes for Fingerprint Recognition Systems” in ACM Comput. Surv., Vol. 27, pp. 28:1-28:36, 2014.
[5] Arstechnica, “Brazilian docs fool biometrics scanners with bag full of fake fingers”, Available at:, 2013. Accessed on: 9 June 2020.
[6] Arstechnica, “Anyone can fingerprint unlock a Galaxy S10--just grab a clear phone case”, Available at:, 2019. Accessed on: 9 June 2020.
[7] T. Matsumoto, H. Matsumoto, K. Tamada, and S. Hoshino, “Impact of artificial “gummy” fingers on fingerprint systems”, in Proceedings of SPIE - The International Society for Optical Engineering, Vol. 4677, 2002.
[8] P. Bontrager, A. Roy, J. Togelius, N. Memon and A. Ross, "DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution" in 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1-9, 2018.
[9] A. Jain, Y. Chen, and M. Demirkus,” Pores and Ridges: Fingerprint Matching Using Level 3 Features” in 18th International Conference on Pattern Recognition (ICPR’06), pp. 477-480, 2006.
[10] K. Fukushima, “Neocognitron A Hierarchical Neural Network Capable of Visual Pattern Recognition”, Neural Networks, Vol. 1, pp. 119-130, 1988.
[11] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition” in Proceedings of the IEEE., Vol. 86, pp. 2278-2324, 1998.
[12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Learning Convolutional Neural Networks” in Advances in neural information processing systems 25(2), 2012.
[13] ImageNet, “ImageNet Large Scale Visual Recognition Challenge”, Available at:, 2015. Accessed on: 9 June 2020.
[14] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks For Large-Scale Image Recognition”, ArXiv, 2014.
[15] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
[16] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2016.
[17] E. Park, W. Kim, Q. Li, J. Lim, and H. Kim, “Fingerprint Liveness Detection Using CNN Features of Random Sample Patches” in 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1-4, 2016.
[18] R. F. Nogueira, R. de A. Lotufo, and R. C. Machado, “Fingerprint Liveness Detection Using Convolutional Neural Networks”. in IEEE Transactions on Information Forensics and Security, Vol.11, No.6, pp. 1206-1213, 2016.
[19] D. Uliyan, S. Sadeghi, and H. A. Jalab, “Anti-spoofing method for fingerprint recognition using patch based deep learning machine” in Engineering Science and Technology, an International Journal, 2019.
[20] T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4396-4405, 2019.
[21] Github, “StyleGAN - Official TensorFlow Implementation”, Available at:, 2015. Accessed on: 27 Oct 2020.
[22] LivDet, “LivDet Databases”, Available at:, Accessed on 2009.