Perspective Neural Network Algorithms for Dynamic Biometric Pattern Recognition in the Space of Interdependent Features

A model of neurons for biometric authentication, capable of efficient processing of highly dependent features, based on the agreement criteria (Gini, Сramer-von-Mises, Kolmogorov-Smirnov, the maximum of intersection areas of probability densities) is proposed. An experiment was performed on comparin...

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Bibliographic Details
Published in:2018 Dynamics of Systems, Mechanisms and Machines : 13-15 November 2018, Omsk, Russia pp. 1 - 12
Main Authors: Sulavko, A. E., Zhumazhanova, S. S., Fofanov, G. A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2018
Subjects:
"wide hybrid" neural networks
Artificial neural networks
Authentication
Biological neural networks
Deep learning
handwriting signature features
informativeness of biometric features
Neurons
Password
random variable distribution law
small training sample
statistical functionals
Training
voice parameters
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Summary:A model of neurons for biometric authentication, capable of efficient processing of highly dependent features, based on the agreement criteria (Gini, Сramer-von-Mises, Kolmogorov-Smirnov, the maximum of intersection areas of probability densities) is proposed. An experiment was performed on comparing the efficiency of neurons based on the proposed model and neurons on the basis of difference and hyperbolic Bayesian functionals capable of processing highly dependent biometric data. Variants of construction of hybrid neural networks, that can be trained on a small number of examples of a biometric pattern (about 20), are suggested. An experiment was conducted to collect dynamic biometric patterns, in the experiment 90 people entered handwritten and voice patterns during a month. Intermediate results on recognition of subjects based on hybrid neural networks were obtained. Number of errors in verification of a signature (handwritten password) was less than 2%, verification of a speaker by a fixed passphrase was less than 6%. The testing was carried out on biometric samples, obtained after some time period after the formation of training sample.
DOI:10.1109/Dynamics.2018.8601440