Integration of Biometric and Electronic Signatures Using Neural Network Algorithms
Keywords:
artificial neural networks, biometrics, electronic signature, pattern recognition
Abstract
This article provides arguments regarding the possibility of using modern methods of pattern recognition in problems of biometric authentication: fuzzy extractors, artificial multilayer neural networks, deep learning methods, as well as convolutional, evolutionary, small, wide, hybrid neural networks. The results of our own research in this area are presented. Two methods are proposed for integrating biometric and electronic signatures based on dynamic signature parameters, as well as face and keyboard handwriting parameters.
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References
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2. Lozhnikov P.S., Sulavko A.E., Eremenko A.V., Volkov D.A. Methods of Generating Key Sequences based on Parameters of Handwritten Passwords and Signatures // Information. - 2016. - No. 7 (4). - P. 59; DOI: 3390 / info7040059.
3. Vasiliev V.I., Lozhnikov P.S., Sulavko A.E., Zhumazhanova S.S. Assessment of the identification capabilities of biometric features from standard peripheral equipment // Information security issues. - 2016. - No. - S. 12-20.
4. Lozhnikov PS, Sulavko AE, Buraya EV, Pisarenko V.Yu. Authentication of computer users based on keyboard handwriting and facial features // Cybersecurity Issues. - 2017. - No. 3. - S. 24-34.
5. Lozhnikov P.S., Sulavko, A.E., Volkov D.A. Application of Noise Tolerant Code to Biometric Data to Verify the Authenticity of Transmitting Information / Control and Communications (SIBCON), 21-May 2015, Omsk, Russia. - P.1-3; DOI: 10.1109 / SIBCON.2015.7147126.
6. Lozhnikov P.S., Sulavko A.E., Eremenko A.V., Buraya E.V. Methods of Generating Key Sequences Based on Keystroke Dynamics // X
7. International IEEE Scientific and Technical Conference “Dynamics of Systems, Mechanisms and Machines” (Dynamics), November 15-17, 2016, Omsk, Russia. - P. 1-5; DOI: 10.1109 / Dynamics.2016.7819038.
8. Yasuoka Y., Shinomiya Y., Hoshino Y. Evaluation of Optimization Methods for Neural Network // Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems, 25-28 August 2016, Sapporo, Japan; DOI: 10.1109 / SCIS-ISIS.2016.0032.
9. Hafemann L. G. et al. Writer-independent Feature Learning for Offline Signature Verification Using Deep Convolutional Neural Networks //2016 International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016. - P. 2576-2583; DOI: 10.1109 / IJCNN.2016.7727521
10. Kůrková V., Sanguineti M. Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks // Neural Networks. – 2017. – Vol. 91.– P. 34-41.
11. Kůrková V., Sanguineti M. Model Complexities of Shallow Networks Representing Highly Varying Functions // Neurocomputing. – 2016. – Vol. 171. – P. 598-604.
12. Rogers L.L., Dowla F.U.: Optimization of Groundwater Remediation Using Artificial Neural Networks with Parallel Solute Transport Modeling //. Water Resources Research. – 1994. – Vol. 30(2). – P. 457– 481.
13. Stanley K.O. Efficient Evolution of Neural Networks Through Complexification. PhD Thesis. Department of Computer Sciences, The University of Texas at Austin, 2004.
14. Koutn´ık J., Schmidhuber J., Gomez F. Evolving Deep Unsupervised Convolutional Networks for Vision-Based Reinforcement Learning // 2014 Annual Conference on Genetic and Evolutionary Computation. – 2014. – P. 541–548.
15. Volchikhin V.I., Ivanov A.I., Funtikov V.A. Bystryye algoritmy obucheniya neyrosetevykh mekhanizmov biometriko-kriptograficheskoy zashchity informatsii. Monografiya. Penza: Izd-vo Penzenskogo gosudarstvennogo universiteta. – 2005. – S. 273.
16. Sulavko A.E., Fedotov A.A., Eremenko A.V. Users’ Identification through Keystroke Dynamics Based on Vibration Parameters and Keyboard Pressure // XI International IEEE Scientific and Technical Conference “Dynamics of Systems, Mechanisms and Machines” (Dynamics), 14-16 November, 2017, Omsk, Russia. – P. 1-7.
17. Ivanov A.I. Mnogomernaya neyrosetevaya obrabotka biometricheskikh dannykh s programmnym vosproizvedeniyem effektov kvantovoy superpozitsii. Penza: Izd-vo PNIEI. – 2016. – S. 133.
18. Ivanov A.I., Lozhnikov P.S., Sulavko A.Ye. Otsenka nadezhnosti verifikatsii avtografa na osnove iskusstvennykh neyronnykh setey, setey mnogomernykh funktsionalov Bayyesa i setey kvadratichnykh form // Komp’yuternaya optika. – 2017. – T. 41. – №5. – S.765-774; DOI: 10.18287/2412-6179-2017-41-5-765-774.
19. Ivanov A.I., Lozhnikov P.S., Vyatchanin S.E. Comparable Estimation of Network Power for Chisquared Pearson Functional Networks and Bayes Hyperbolic Functional Networks while Processing Biometric Data / Control and Communications (SIBCON), 29-30 June 2017, Astana, Kazakhstan. – P.1-3; DOI: 10.1109/ SIBCON.2017.7998435.
20. Akhmetov B.S., Ivanov A.I., Funtikov V.A., Bezyayev A.V., Malygina Ye.A. Tekhnologiya ispol’zovaniya bol’shikh neyronnykh setey dlya preobrazovaniya nechetkikh biometricheskikh dannykh v kod klyucha dostupa: Monografiya. / Almaty: TOO «Izdatel’stvo LEM». – 2014. – S. 144.
21. Bezyayev A. V., Ivanov A. I., Funtikova YU. V. Optimizatsiya struktury samokorrektiruyushchegosya biokoda, khranyashchego sindromy oshibok v vide fragmentov khesh-funktsiy // Vestnik UrFO. Bezopasnost’ v informatsionnoy sfere. – 2014. – № 3(13). – S. 4 -13.
22. Lozhnikov P.S., Sulavko A.Ye. Generation of a Biometrically Activated Digital Signature Based on Hybrid Neural Network Algorithms // Journal of Physics: Conf. Series. –2018. – № 1050; DOI: 10.1088/1742-6596/1050/1/012047
Published
2022-05-04
How to Cite
Sunbula, M., & Mansurbek, N. (2022). Integration of Biometric and Electronic Signatures Using Neural Network Algorithms. Central Asian Journal of Theoretical and Applied Science, 3(4), 127-136. https://doi.org/10.17605/OSF.IO/CYQFS
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
