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This article is cited in 3 scientific papers (total in 3 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks
A. E. Sulavko Omsk State Technical University, Omsk, Russia
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.
Received: 09.05.2019 Accepted: 16.10.2019
Citation:
A. E. Sulavko, “Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks”, Computer Optics, 44:1 (2020), 82–91
Linking options:
https://www.mathnet.ru/eng/co765 https://www.mathnet.ru/eng/co/v44/i1/p82
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Abstract page: | 230 | Full-text PDF : | 83 | References: | 32 |
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