Journal Menu
> Issues > Rubrics > About journal > Authors > About the Journal > Requirements for publication > Council of Editors > Peer-review process > Policy of publication. Aims & Scope. > Article retraction > Ethics > Copyright & Licensing Policy > Digital archiving policy > Open Access Policy > Open access publishing costs > Article Identification Policy > Plagiarism check policy
Journals in science databases
About the Journal

Публикация за 72 часа - теперь это реальность!
При необходимости издательство предоставляет авторам услугу сверхсрочной полноценной публикации. Уже через 72 часа статья появляется в числе опубликованных на сайте издательства с DOI и номерами страниц.
По первому требованию предоставляем все подтверждающие публикацию документы!
MAIN PAGE > Back to contents
Cybernetics and programming

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

Grishentsev Aleksei Yurevich

PhD in Technical Science

Associate Professor, ITMO University

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

Savchenko-Novopavlovskaya Sof'ya Leonidovna

master, ITMO University

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

Nechaeva Natalya Viktorovna

master, ITMO University

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

Eliseeva Valeria Valeryevna

master, ITMO University

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

Artemeva Viktoriia Denisovna

student, Kant Baltic Federal University 

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



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



Article was received:


Review date:


Publish date:


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

Global'noe issledovanie utechek konfidentsial'noi informatsii v 2015 godu // Analiticheskii tsentr kompanii InfoWatch. [Elektronnyi resurs]. URL: https://www.infowatch.ru/sites/default/files/report/analytics/russ/InfoWatch_Global_Report_2015.pdf?rel=1 (data obrashcheniya: 24.04.2016).
Godovoi otchet Cisco po informatsionnoi bezopasnosti za 2016 god // Internet portal Cisco.com [Elektronnyi resurs] URL: www.cisco.com/c/dam/m/ru_ru/internet.../security/cisco_2016_asr_011116_ru.pdf (data obrashcheniya: 01.07.2016)
Dapinder Singh, Neha Sharma Review on Enhanced Offline Signature Recognition Using Neural Network and LDA // International Journal of Advanced Research in Computer Science and Software Engineering. 2015. №5. S. 850-854.
Fahad Layth Malallah, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Olasimbo Ayodeji Arigbabu, Vahab Iranmanesh, Salman Yussof Online Handwritten Signature Recognition by Length Normalization using Up-Sampling and Down-Sampling // International Journal of Cyber-Security and Digital Forensics. 2015. №4. S. 302-313.
Paigwar Shikha, Shukla Shailja Neural Network Based Offline Signature Recognition and Verification System // Research Journal of Engineering Sciences. 2013. №2. S. 11-15.
Dawid Połap, Marcin Wo´zniak Flexible Neural Network Architecture for Handwritten Signatures Recognition // Intl journal of electronics and telecommunications. 2016. №62. S. 197–202.
Alan McCabe, Jarrod Trevathan, Wayne Read Neural Network-based Handwritten Signature Verification // Journal Of Computers. 2008. №3. S. 9-22.
Komal Pawar, Tanuja Dhope Static Signature Verification and Recognition using Neural Network Approach-A Survey // European Journal of Advances in Engineering and Technology. 2015. №2. S. 46-50.
Pradeep Kumar, Shekhar Singh, Ashwani Garg, Nishant Prabhat Hand Written Signature Recognition & Verification using Neural Network // International Journal of Advanced Research in Computer Science and Software Engineering. 2013. №3. S. 558-565.
Ashwini Pansare, Shalini Bhatia Handwritten Signature Verification using Neural Network // International Journal of Applied Information Systems. 2012. №1. S. 44-49.
Manoj Kumar Signature Verification Using Neural Network // International Journal on Computer Science and Engineering. 2012. №4. S. 1498-1504.
Musa Mailah, Lim Boon Han Biometric signature verification using pen position, time, velocity and pressure parameters // Jurnal Teknologi. 2008. №48. S. 35-54.
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sickinger, Roopak Shah Signature Verification using a "Siamese" Time Delay Neural Network // American Telephone and Telegraph Company. 1994. S. 737-744.
Julio Cesar Martínez-Romo , Francisco Javier Luna-Rosas,Miguel Mora-González On-line signature verification based on optimal feature representation and neural-network-driven fuzzy reasoning // MICAI 2009: Advances in Artificial Intelligence. 2009. №5845. S. 246-257.
Meetu Sangwan, Mr. Daulat Sihag Handwritten Signature Recognition, Verification and Dynamic Updation using Neural Network // International Journal of Advanced Research in Computer and Communication Engineering. 2015. №4. S. 218-221.
Debnath Bhattacharyya, Tai-Hoon Kim Signature Recognition using Artificial Neural Network // Advances in Computational Intelligence, Man-Machine Systems and Cybernetics. 2010. S. 183-187.
Prathiba M.K., Dr. L. Basavaraj Online handwritten signature verification system: A Review // International Journal of Emerging Trends & Technology in Computer Science. 2014. №3. S. 263-267.
O.C Abikoye, M.A Mabayoje, R. Ajibade Offline Signature Recognition & Verification using Neural Network // International Journal of Computer Applications. 2011. №35. S. 44-51.
Link to this article

You can simply select and copy link from below text field.

Other our sites:
Official Website of NOTA BENE / Aurora Group s.r.o.
"History Illustrated" Website