Finger-Vein Classification Using Granular Support Vector Machine

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017074" target="_blank" >RIV/62690094:18450/20:50017074 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-41964-6_27" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-41964-6_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-41964-6_27" target="_blank" >10.1007/978-3-030-41964-6_27</a>

Result language

angličtina

Original language name

Finger-Vein Classification Using Granular Support Vector Machine

Original language description

The protection of control and intelligent systems across networks and interconnected components is a significant concern. Biometric systems are smart systems that ensure the safety and protection of the information stored across these systems. A breach of security in a biometric system is a breach in the overall security of data and privacy. Therefore, the advancement in improving the safety of biometric systems forms part of ensuring a robust security system. In this paper, we aimed at strengthening the finger vein classification that is acknowledged to be a fraud-proof unimodal biometric trait. Despite several attempts to enhance finger-vein recognition by researchers, the classification accuracy and performance is still a significant concern in this research. This is due to high dimensionality and invariability associated with finger-vein image features as well as the inability of small training samples to give high accuracy for the finger-vein classifications. We aim to fill this gap by representing the finger vein features in the form of information granules using an interval-based hyperbox granular approach and then apply a dimensionality reduction on these features using principal component analysis (PCA). We further apply a granular classification using an improved granular support vector machine (GSVM) technique based on weighted linear loss function to avoid overfitting and yield better generalization performance and enhance classification accuracy. We named our approach PCA-GSVM. The experimental results show that the classification of finger-vein granular features provides better results when compared with some state-of-the-art biometric techniques used in multimodal biometric systems. © 2020, Springer Nature Switzerland AG.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-030-41963-9

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

12

Pages from-to

309-320

Publisher name

Springer

Place of publication

Cham

Event location

Thailand

Event date

May 23, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000611576500027