Approaches to online handwritten signature verification
Handwritten signature is one of the most common methods of biometric authentication, where static and dynamic signature characteristics are used to confirm the user's identity. The existing developments are based on various technologies, such as the neural network, the hidden Markov model, and...
| Published in: | Безопасность информационных технологий |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Joint Stock Company "Experimental Scientific and Production Association SPELS
2020-06-01
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| Subjects: | |
| Online Access: | https://bit.mephi.ru/index.php/bit/article/view/1272 |
| _version_ | 1851879958402564096 |
|---|---|
| author | Anastasia V. Beresneva Anna V. Epishkina |
| author_facet | Anastasia V. Beresneva Anna V. Epishkina |
| author_sort | Anastasia V. Beresneva |
| collection | DOAJ |
| container_title | Безопасность информационных технологий |
| description | Handwritten signature is one of the most common methods of biometric authentication, where static and dynamic signature characteristics are used to confirm the user's identity. The existing developments are based on various technologies, such as the neural network, the hidden Markov model, and machine learning algorithms. This topic is rapidly developing, new approaches and algorithms for solving the problem improve the accuracy of verification and learning speed. The purpose of this study is to analyze existing approaches to the signature verification. The most promising algorithm will be used as the basis for the developed authentication system based on a handwritten signature. |
| format | Article |
| id | doaj-art-709b2ea0bfcd41219151ebfb2a2c5f98 |
| institution | Directory of Open Access Journals |
| issn | 2074-7128 2074-7136 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | Joint Stock Company "Experimental Scientific and Production Association SPELS |
| record_format | Article |
| spelling | doaj-art-709b2ea0bfcd41219151ebfb2a2c5f982025-08-19T22:13:40ZengJoint Stock Company "Experimental Scientific and Production Association SPELSБезопасность информационных технологий2074-71282074-71362020-06-01272788510.26583/bit.2020.2.061190Approaches to online handwritten signature verificationAnastasia V. Beresneva0Anna V. Epishkina1National Nuclear Research University MEPHI (Moscow Engineering Physics Institute)National Nuclear Research University MEPHI (Moscow Engineering Physics Institute)Handwritten signature is one of the most common methods of biometric authentication, where static and dynamic signature characteristics are used to confirm the user's identity. The existing developments are based on various technologies, such as the neural network, the hidden Markov model, and machine learning algorithms. This topic is rapidly developing, new approaches and algorithms for solving the problem improve the accuracy of verification and learning speed. The purpose of this study is to analyze existing approaches to the signature verification. The most promising algorithm will be used as the basis for the developed authentication system based on a handwritten signature.https://bit.mephi.ru/index.php/bit/article/view/1272verification, authentication, biometric authentication, handwritten signature, machine learning, neural network. |
| spellingShingle | Anastasia V. Beresneva Anna V. Epishkina Approaches to online handwritten signature verification verification, authentication, biometric authentication, handwritten signature, machine learning, neural network. |
| title | Approaches to online handwritten signature verification |
| title_full | Approaches to online handwritten signature verification |
| title_fullStr | Approaches to online handwritten signature verification |
| title_full_unstemmed | Approaches to online handwritten signature verification |
| title_short | Approaches to online handwritten signature verification |
| title_sort | approaches to online handwritten signature verification |
| topic | verification, authentication, biometric authentication, handwritten signature, machine learning, neural network. |
| url | https://bit.mephi.ru/index.php/bit/article/view/1272 |
| work_keys_str_mv | AT anastasiavberesneva approachestoonlinehandwrittensignatureverification AT annavepishkina approachestoonlinehandwrittensignatureverification |
