Finger-Vein Classification Using Granular Support Vector Machine

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Finger-Vein Classification Using Granular Support Vector Machine

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Finger-Vein Classification Using Granular Support Vector Machine

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

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 statě ve sborníku

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

Počet stran výsledku

12

Strana od-do

309-320

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Thailand

Datum konání akce

23. 5. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000611576500027