Signature analysis system using a convolutional neural network

• Autorzy: Winnicka Alicja , Kęsik Karolina , Połap Dawid

Identyfikatory

DOI

10.15439/2019F28

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

biometric access control; convolutional neural nets; digital signature; handwriting recognition

PL

biometryczna kontrola dostępu; konwolucyjna sieć neuronowa; podpis cyfrowy; rozpoznawanie pisma odręcznego

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 287--290
• Opis fizyczny: Bibliogr. 16 poz., wz., rys., wykr.

Twórcy

autor

Winnicka Alicja

• Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

autor

Kęsik Karolina

• Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

autor

Połap Dawid

• Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

Bibliografia

• 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.

Uwagi

1. Authors acknowledge contribution to this project to the Diamond Grant No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education.

2. Track 2: Computer Science & Systems

3. Technical Session: 4th International Workshop on Language Technologies and Applications

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).