Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F28

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 287–290 (2019)

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Abstract. Identity verification using biometric methods has been used for many years. A special case is a handwritten signature made on a digital device or piece of paper. For the digital analysis and verification of its authenticity, special methods are needed. Unfortunately, this is a rather complicated task that quite often requires complex processing techniques. In this paper, we propose a system of signatures verification consisting of two stages. In the first one, a signature pattern is created. Thanks to this, the first attempt to verify identity takes place. In the case of approval, the second stage is followed by the processing of a graphic sample containing a signature by the convolutional neural network. The proposed technique has been described, tested and discussed due to its practical use.

References

1. I. Rocco, R. Arandjelovic, and J. Sivic, “Convolutional neural network architecture for geometric matching,” IEEE transactions on pattern analysis and machine intelligence, 2018.
2. V. Nourani, S. Mousavi, D. Dabrowska, and F. Sadikoglu, “Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media,” Journal of hydrology, vol. 548, pp. 569–587, 2017.
3. D. Dabrowska, ̨ R. Kucharski, and A. J. Witkowski, “The representativity index of a simple monitoring network with regular theoretical shapes and its practical application for the existing groundwater monitoring network of the tychy-urbanowice landfills, poland,” Environmental Earth Sciences, vol. 75, no. 9, p. 749, 2016.
4. A. Venčkauskas, R. Damaševičius, R. Marcinkevičius, and A. Karpavičius, “Problems of authorship identification of the national language electronic discourse,” in International Conference on Information and Software Technologies. Springer, 2015, pp. 415–432.
5. R. Damaševičius, R. Maskeliūnas, E. Kazanavičius, and M. Woźniak, “Combining cryptography with eeg biometrics,” Computational intelligence and neuroscience, vol. 2018, 2018.
6. R. Damaševičius, R. Maskeliūnas, A. Venčkauskas, and M. Woźniak, “Smartphone user identity verification using gait characteristics,” Symmetry, vol. 8, no. 10, p. 100, 2016.
7. R. Tolosana, R. Vera-Rodriguez, J. Fierrez, and J. Ortega-Garcia, “Exploring recurrent neural networks for on-line handwritten signature biometrics,” IEEE Access, vol. 6, no. 5128-5138, pp. 1–7, 2018.
8. M. Elhoseny, A. Nabil, A. E. Hassanien, and D. Oliva, “Hybrid rough neural network model for signature recognition,” in Advances in Soft Computing and Machine Learning in Image Processing. Springer, 2018, pp. 295–318.
9. M. Diaz, A. Fischer, M. A. Ferrer, and R. Plamondon, “Dynamic signature verification system based on one real signature,” IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 228–239, 2018.
10. G. L. Masala, P. Ruiu, and E. Grosso, “Biometric authentication and data security in cloud computing,” in Computer and Network Security Essentials. Springer, 2018, pp. 337–353.
11. Z. Sroczyński, “Actiontracking for multi-platform mobile applications,” in Computer Science On-line Conference. Springer, 2017, pp. 339–348.
12. A. Bier and Z. Sroczynski, “Towards semantic search for mathematical notation,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2018, pp. 465–469.
13. N. Merhav, “Ensemble performance of biometric authentication systems based on secret key generation,” IEEE Transactions on Information Theory, 2018.
14. K. Zhou and J. Ren, “Passbio: Privacy-preserving user-centric biometric authentication,” IEEE Transactions on Information Forensics and Security, 2018.
15. P. Gupta and P. Gupta, “Multibiometric authentication system using slap fingerprints, palm dorsal vein, and hand geometry,” IEEE Transactions on Industrial Electronics, vol. 65, no. 12, pp. 9777–9784, 2018.
16. H. Huang, C. Wang, and B. Dong, “Nostalgic adam: Weighing more of the past gradients when designing the adaptive learning rate,” arXiv preprint https://arxiv.org/abs/1805.07557, 2018.