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Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks

A.E. Sulavko 1

Omsk State Technical University, Omsk, Russia

 PDF, 899 kB

DOI: 10.18287/2412-6179-CO-567

Pages: 82-91.

Full text of article: Russian language.

The paper addresses a problem of highly reliable biometric authentication based on converters of secret biometric images into a long key or password, as well as their testing on relatively small samples (thousands of images). Static images are open, therefore with remote authentication they are of a limited trust. A process of calculating the biometric parameters of voice and handwritten passwords is described, a method for automatically generating a flexible hybrid network consisting of various types of neurons is proposed, and an absolutely stable algorithm for network learning using small samples of “Custom” (7-15 examples) is developed. A method of a trained hybrid "biometrics-code" converter based on knowledge extraction is proposed. Low values of FAR (false acceptance rate) are achieved.

hybrid networks, quadratic forms, Bayesian functionals, handwritten passwords, voice parameters, wide neural networks, biometrics-code converters, protected neural containers.

Sulavko AE. Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks. Computer Optics 2020; 44(1): 82-91. DOI: 10.18287/2412-6179-CO-567.

This work is supported by the Russian Science Foundation under grant №17-71-10094.


  1. Ivanov AI, Lozhnikov PS, Sulavko AE. Evaluation of signature verification reliability based on artificial neural networks, Bayesian multivariate functional and quadratic forms. Computer Optics 2017; 41(5): 765-774.
  2. Hafemann LG, Sabourin R, Oliveira LS. Writer-inde­pendent feature learning for offline signature verification using deep convolutional neural networks. International Joint Conference on Neural Networks 2016: 2576-2583.
  3. Souza VLF, Oliveira ALI, Sabourin R. A writer-independent approach for offline signature verification using deep convolutional neural networks features. 7th Brazilian Conference on Intelligent Systems 2018: 212-217.
  4. Tachibana H, Uenoyama K, Aihara Sh. Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018: 4784-4788.
  5. Mai G, Cao K, Yuen PC, Jain AK. On the reconstruction of face images from deep face templates. Trans Patt Anal Machine Intell 2019; 41(5): 1188-1202.
  6. Hafemann LG, Sabourin R, Oliveira LS. Characterizing and evaluating adversarial examples for offline handwritten signature verification. IEEE Transactions on Information Forensics and Security 2019; 14(8): 2153-2166. DOI: 10.1109/TIFS.2019.2894031.
  7. Gulov VP, Ivanov AI, Yazov YuK, Korneev OV. Perspective of neuro network protection of cloud services through biometric deployment of personal information on the example of medical electronic history of disease (Brief review of the literature) [In Russian]. Journal of New Medical Technologies 2017; 24(2): 220-225.
  8. Ahmetov BS, Volchihin VI, Ivanov AI, Malygin AYu. Algorithms for testing biometric-neural network information protection mechanisms [In Russian]. Almaty: “KazNTU imeni K I Satpaeva” Publisher; 2013.
  9. Lozhnikov PS. Hybrid workflow biometric protection [In Russian]. “SO RAN” Publisher; 2017.
  10. Torfi A, Dawson J, Nasrabadi NM. Text-independent speaker verification using 3D convolutional neural networks. IEEE International Conference on Multimedia and Expo (ICME) 2018: 1-6.
  11. Akhmetov, BS, Ivanov, AI, Alimseitova, ZK Training of neural network biometry-code converters. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences 2018: 61-68.
  12. Malygin A, Seilova N, Boskebeev K, Alimseitova Zh. Application of artificial neural networks forhandwritten biometric images recognition Computer Modelling and New Technologies, 2017, 21(1), 31-38.
  13. Gorshkov YuG. Wavelet-based speech and acoustic biomedical signal processing. Moscow: “Radiotekhnika” Publisher; 2017.
  14. Lukic Y, Vogt C, Dürr O, Stadelmann T. Speaker identification and clustering using convolutional neural networks. 26th International Workshop on Machine Learning for Signal Processing 2016: 1-6.
  15. Zhilyakov EG, Firsova AA, Chekanov NA. Algorithms for detecting the fundamental tone of speech signals. Belgorod State University Scientific Bulletin; Series Economics; Computer Science 2012; 1(120:21): 135-143.
  16. Vasilyev VI, Sulavko AE, Zhumazhanova SS, Borisov RV. Identification of the psychophysiological state of the user based on hidden monitoring in computer systems. Scientific and Technical Information Processing 2018; 45(6): 398-410.
  17. Sulavko AE, Volkov DA, Zhumazhanova SS, Borisov RV. Subjects authentication based on secret biometric patterns using wavelet analysis and flexible neural networks. XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering 2018: 218-227.
  18. Sulavko AE, Zhumazhanova SS, Fofanov GА. Perspective neural network algorithms for dynamic biometric pattern recognition in the space of interdependent features. Dynamics of Systems, Mechanisms and Machines 2018: 1-12.
  19. Ivanov, AI Lozhnikov, PS Vyatchanin SE. Comparable estimation of network power for chi-squared Pearson functional networks and Bayes hyperbolic functional networks while processing biometric data. Control and Communications 2017: 1-3.
  20. Sulavko AE, Zhumazhanova SS. Biometric pattern recognition using wide networks of gravity proximity measures. J Phys Conf Ser 2018; 1050: 012082.
  21. Vasilyev VI, Lozhnikov PS, Sulavko AE, Fofanov GА, Zhumazhanova SS. Flexible fast learning neural networks and their application for building highly reliable biometric cryptosystems based on dynamic features. IFAC-PapersOnLine 2018; 51(30): 527-532.
  22. Larcher A, Lee KA, Ma B, Li H. Text-dependent speaker verification: Classifiers, databases and RSR2015. Speech Communication 2014; 60: 56-77.
  23. Diaz M, Ferrer MA, Impedovo D, Malik MI, Pirlo G, Plamondon R. A perspective analysis of handwritten signature technology. ACM Computing Surveys 2019; 51(6): 117. Lozhnikov P, Sulavko A.
  24. Cloud biometrical system identification through handwriting dynamics “SignToLogin” Certificate of registration No. TX 7-640-429 from 18.12.2012.


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