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

A.E. Sulavko 1

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.

Abstract:
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.

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

Citation:
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.

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

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