Thermal-based gender recognition using drones: advancing biometric recognition in challenging outdoor environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39921878" target="_blank" >RIV/00216275:25410/24:39921878 - isvavai.cz</a>

Výsledek na webu

<a href="https://cdnsciencepub.com/doi/10.1139/dsa-2023-0075" target="_blank" >https://cdnsciencepub.com/doi/10.1139/dsa-2023-0075</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1139/dsa-2023-0075" target="_blank" >10.1139/dsa-2023-0075</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Thermal-based gender recognition using drones: advancing biometric recognition in challenging outdoor environments

Popis výsledku v původním jazyce

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNNs) (AlexNet, VGG-16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images is used for training the CNNs, while a own created dataset of thermal images obtained by the drone is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet), and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.

Název v anglickém jazyce

Thermal-based gender recognition using drones: advancing biometric recognition in challenging outdoor environments

Popis výsledku anglicky

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNNs) (AlexNet, VGG-16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images is used for training the CNNs, while a own created dataset of thermal images obtained by the drone is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet), and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Drone Systems and Applications

ISSN

2564-4939

e-ISSN

2564-4939

Svazek periodika

12

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

CA - Kanada

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

001264010100001

EID výsledku v databázi Scopus

2-s2.0-85199621964