Investigation of Bilateral Similarity in Central Retinal Blood Vessels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142891" target="_blank" >RIV/00216305:26230/21:PU142891 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9409039" target="_blank" >https://ieeexplore.ieee.org/document/9409039</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3074514" target="_blank" >10.1109/ACCESS.2021.3074514</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Investigation of Bilateral Similarity in Central Retinal Blood Vessels

Popis výsledku v původním jazyce

While our left and right eyes clearly have a high degree of external bilateral similarity, it is less obvious to what degree they have internal bilateral similarity. This is especially true for the central retinal blood vessels (CRBVs), which are responsible for supplying blood to retinas and also can be used as a strong biometric. In this paper, we investigate whether the CRBVs of the left and right retinas possess strong enough bilateral similarity so that we reliably tell whether a  pair of the left and right retinas belong to a single subject. We evaluate and analyse the performance of both human- and deep neural network-based bilateral verification by experimenting on two publicly available data sets.

Název v anglickém jazyce

Investigation of Bilateral Similarity in Central Retinal Blood Vessels

Popis výsledku anglicky

While our left and right eyes clearly have a high degree of external bilateral similarity, it is less obvious to what degree they have internal bilateral similarity. This is especially true for the central retinal blood vessels (CRBVs), which are responsible for supplying blood to retinas and also can be used as a strong biometric. In this paper, we investigate whether the CRBVs of the left and right retinas possess strong enough bilateral similarity so that we reliably tell whether a  pair of the left and right retinas belong to a single subject. We evaluate and analyse the performance of both human- and deep neural network-based bilateral verification by experimenting on two publicly available data sets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2021

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

2021

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

63012-63028

Kód UT WoS článku

000645855900001

EID výsledku v databázi Scopus

2-s2.0-85104683439