ASSESSING THE ROBUSTNESS OF FACIAL CLASSIFICATION METHODS IN THE BIOMETRIC IDENTIFICATION AREA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27200%2F25%3A10259495" target="_blank" >RIV/61989100:27200/25:10259495 - isvavai.cz</a>

Výsledek na webu

<a href="https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=10254" target="_blank" >https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=10254</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/sgem2025/2.1/s07.01" target="_blank" >10.5593/sgem2025/2.1/s07.01</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ASSESSING THE ROBUSTNESS OF FACIAL CLASSIFICATION METHODS IN THE BIOMETRIC IDENTIFICATION AREA

Popis výsledku v původním jazyce

This article focuses on the effectiveness and robustness of facial classification systems in the field of biometric identification. Artificial intelligence is increasingly becoming a part of everyday life, with more and more users employing it across various domains. In the field of security, AI is used, for instance, in cybersecurity and risk analysis. It is also integrated into surveillance systems, particularly for facial recognition. A comparative analysis of three convolutional neural networks—GoogLeNet, ResNet-101, and DenseNet-201—was conducted in this study using the MATLAB simulation environment. These CNNs were pre-trained and subsequently tested from several perspectives, including performance, training time, and validation accuracy. The collected data served as a basis for comparing the networks with one another and were also used for further analysis of training and output evaluation. The results can form the basis for further research and can be compared with a possible study in which real photographs with higher noise were used. The results can also be applied to enhance electronic security systems, such as access control for mines and geologically significant sites.

Název v anglickém jazyce

ASSESSING THE ROBUSTNESS OF FACIAL CLASSIFICATION METHODS IN THE BIOMETRIC IDENTIFICATION AREA

Popis výsledku anglicky

This article focuses on the effectiveness and robustness of facial classification systems in the field of biometric identification. Artificial intelligence is increasingly becoming a part of everyday life, with more and more users employing it across various domains. In the field of security, AI is used, for instance, in cybersecurity and risk analysis. It is also integrated into surveillance systems, particularly for facial recognition. A comparative analysis of three convolutional neural networks—GoogLeNet, ResNet-101, and DenseNet-201—was conducted in this study using the MATLAB simulation environment. These CNNs were pre-trained and subsequently tested from several perspectives, including performance, training time, and validation accuracy. The collected data served as a basis for comparing the networks with one another and were also used for further analysis of training and output evaluation. The results can form the basis for further research and can be compared with a possible study in which real photographs with higher noise were used. The results can also be applied to enhance electronic security systems, such as access control for mines and geologically significant sites.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM. Volume 25, Issue 2.1

ISBN

978-619-7603-89-7

ISSN

1314-2704

e-ISSN

1314-2704

Počet stran výsledku

10

Strana od-do

3-12

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

29. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—