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Cybernetics and programming
Reference:

Development of neural network module for user authentication based on handwriting dynamics
Dikii Dmitrii Igorevich

graduate student, ITMO University

197101, Russia, Sankt-Peterburg, g. Cankt-Peterburg, pr. Kronverkskii, 49

dimandikiy@mail.ru
Grishentsev Aleksei Yurevich

PhD in Technical Science

Associate Professor, ITMO University

197101, Russia, g. Saint Petersburg, pr. Kronverkskii, 49

grishentcev@ya.ru
Savchenko-Novopavlovskaya Sof'ya Leonidovna

master, ITMO University

197101, Russia, g. Cankt-Peterburg, pr. Kronverkskii, 49

Novopalych@hotmail.com
Nechaeva Natalya Viktorovna

master, ITMO University

197101, Russia, g. Saint Petersburg, pr. Kronverkskii, 49

exotica1980@mail.ru
Eliseeva Valeria Valeryevna

master, ITMO University

197101, Russia, g. Saint Petersburg, pr. Kronverkskii, 49

valeria_eliseeva@mail.ru
Artemeva Viktoriia Denisovna

student, Kant Baltic Federal University 

238300, Russia, Kaliningradskaya oblast', g. Kaliningrad, ul. A. Nevskogo, 14a

vika_med2019@mail.ru

Abstract.

The article is devoted to the development and investigation of the structure of the neural network module, which is part of the authentication system for users of various information systems analyzing the parameters of handwriting dynamics. The algorithm for learning the neural network module is also considered. The main task that the neural network module should solve is the implementation of a binary classifier based on input characteristic vectors such as the Cartesian coordinates of the handwriting pattern along the abscissa and ordinate axes, as well as time cuts that allow describing the writing speed of the sample. For the structures of the neural network module considered in the experiment, an experiment was performed in which different volumes of handwriting samples were fed to the input in order to determine the most stable. A mathematical model of a neural network module and a genetic algorithm for its learning are described. The article also provides an overview of the structures of neural network modules that are used in other user authentication software for the dynamics of handwriting. The substantiation of the choice of the module structure based on the results of the experiment is presented. The software implementation of the neural network module is implemented in the Java programming language.

Keywords: binary classifier, machine learning, perceptron, genetic algorithm, artificial neural network, handwriting dynamics, authentication, biometrics, signature, password

DOI:

10.25136/2306-4196.2018.1.19801

Article was received:

03-08-2016


Review date:

05-08-2016


Publish date:

27-02-2018


This article written in Russian. You can find full text of article in Russian here .

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