IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27200%2F22%3A10251683" target="_blank" >RIV/61989100:27200/22:10251683 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/22:10251683

Výsledek na webu

<a href="https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=8472&" target="_blank" >https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=8472&</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/sgem2022/2.1/s07.06" target="_blank" >10.5593/sgem2022/2.1/s07.06</a>

Jazyk výsledku

angličtina

Název v původním jazyce

IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION

Popis výsledku v původním jazyce

This article deals with the design and implementation of an intelligent biometric system that allows the detection and classification of a person&apos;s face from static image data and creates a system for evaluating its reliability. In its introductory part, it theoretically describes applied biometrics and biometric systems for security identification and user verification, and also deals with the theory of the description of algorithms for human face detection and recognition. Subsequently, the authors use the MATLAB programming language, which is highly optimized for modern processors and memory architectures, to focus on the implementation and testing of a biometric system using Viola-Jones algorithms and a convolutional neural network with a pre-trained network NetNet. Convolutional neural networks (CNN) are the most recognized and popular deep-learning neural networks, which are based on layers that perform two-dimensional (2D) convolution of input data with learned filters. In the final part there is a discussion where, based on the results of testing, the robustness and efficiency of the proposed intelligent biometric system is objectively evaluated. The results allow for the continued development of other pre-trained artificial neural networks, variable implementations for facial recognition, but also other things, such as the recognition of potentially dangerous people.

Název v anglickém jazyce

IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION

Popis výsledku anglicky

This article deals with the design and implementation of an intelligent biometric system that allows the detection and classification of a person&apos;s face from static image data and creates a system for evaluating its reliability. In its introductory part, it theoretically describes applied biometrics and biometric systems for security identification and user verification, and also deals with the theory of the description of algorithms for human face detection and recognition. Subsequently, the authors use the MATLAB programming language, which is highly optimized for modern processors and memory architectures, to focus on the implementation and testing of a biometric system using Viola-Jones algorithms and a convolutional neural network with a pre-trained network NetNet. Convolutional neural networks (CNN) are the most recognized and popular deep-learning neural networks, which are based on layers that perform two-dimensional (2D) convolution of input data with learned filters. In the final part there is a discussion where, based on the results of testing, the robustness and efficiency of the proposed intelligent biometric system is objectively evaluated. The results allow for the continued development of other pre-trained artificial neural networks, variable implementations for facial recognition, but also other things, such as the recognition of potentially dangerous people.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2022

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 22, Issue 2.1

ISBN

978-619-7603-40-8

ISSN

1314-2704

e-ISSN

—

Počet stran výsledku

8

Strana od-do

43-50

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

2. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—