Hand-Based Biometric System Using Convolutional Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916037" target="_blank" >RIV/00216275:25410/20:39916037 - isvavai.cz</a>

Výsledek na webu

<a href="https://aip.vse.cz/pdfs/aip/2020/01/04.pdf" target="_blank" >https://aip.vse.cz/pdfs/aip/2020/01/04.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18267/j.aip.131" target="_blank" >10.18267/j.aip.131</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hand-Based Biometric System Using Convolutional Neural Networks

Popis výsledku v původním jazyce

Today, data security is an increasingly hot topic, and thus also the security and reliability of end-user identity verification, i.e. authentication. In recent years, banks began to substitute password authentication by more secure ways of authentication because passwords were not considered to be secure enough. Current legislation even forces banks to implement multi-factor authentication of their clients. Banks, therefore, consider using biometric authentication as one of the possible ways. To verify a user&apos;s identity, biometric authentication uses unique biometric characteristics of the user. Examples of such methods are facial recognition, iris scanning, fingerprints, and so on. This paper deals with another biometric feature that could be used for authentication in mobile banking applications; as almost all mobile phones have an integrated camera, hand authentication can make a banking information system more secure and its user interface more convenient. Although the idea of hand biometric authentication is not entirely new and there exist many ways of implementing it, our approach based on using convolutional neural networks is not only innovative, but its results are promising as well. This paper presents a modern approach to identifying users by convolutional neural networks when this type of neural network is used both for hand features extraction and bank user identity validation.

Název v anglickém jazyce

Hand-Based Biometric System Using Convolutional Neural Networks

Popis výsledku anglicky

Today, data security is an increasingly hot topic, and thus also the security and reliability of end-user identity verification, i.e. authentication. In recent years, banks began to substitute password authentication by more secure ways of authentication because passwords were not considered to be secure enough. Current legislation even forces banks to implement multi-factor authentication of their clients. Banks, therefore, consider using biometric authentication as one of the possible ways. To verify a user&apos;s identity, biometric authentication uses unique biometric characteristics of the user. Examples of such methods are facial recognition, iris scanning, fingerprints, and so on. This paper deals with another biometric feature that could be used for authentication in mobile banking applications; as almost all mobile phones have an integrated camera, hand authentication can make a banking information system more secure and its user interface more convenient. Although the idea of hand biometric authentication is not entirely new and there exist many ways of implementing it, our approach based on using convolutional neural networks is not only innovative, but its results are promising as well. This paper presents a modern approach to identifying users by convolutional neural networks when this type of neural network is used both for hand features extraction and bank user identity validation.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Informatica Pragensia

ISSN

1805-4951

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

48-57

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85090222040