Reconstruction and enhancement techniques for overcoming occlusion in facial recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193306" target="_blank" >RIV/00216305:26230/26:0193306 - isvavai.cz</a>

Výsledek na webu

<a href="https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-025-00670-7" target="_blank" >https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-025-00670-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13640-025-00670-7" target="_blank" >10.1186/s13640-025-00670-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reconstruction and enhancement techniques for overcoming occlusion in facial recognition

Popis výsledku v původním jazyce

Facial occlusions in surveillance footage can obscure important features, preventing facial recognition systems from identifying people. This work focuses on reconstructing these missing facial parts using Generative Adversarial Networks (GANs) to improve facial recognition accuracy while maintaining a low false acceptance rate. Additionally, we investigate how the generated images can be further enhanced using various image enhancement methods to boost recognition accuracy. To evaluate the results, we conduct experiments with widely used face embedding models, such as QMagFace and ArcFace, to determine whether image reconstruction and enhancement improve face recognition accuracy.

Název v anglickém jazyce

Reconstruction and enhancement techniques for overcoming occlusion in facial recognition

Popis výsledku anglicky

Facial occlusions in surveillance footage can obscure important features, preventing facial recognition systems from identifying people. This work focuses on reconstructing these missing facial parts using Generative Adversarial Networks (GANs) to improve facial recognition accuracy while maintaining a low false acceptance rate. Additionally, we investigate how the generated images can be further enhanced using various image enhancement methods to boost recognition accuracy. To evaluate the results, we conduct experiments with widely used face embedding models, such as QMagFace and ArcFace, to determine whether image reconstruction and enhancement improve face recognition accuracy.

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

2025

Kód důvěrnosti údajů

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

Název periodika

EURASIP Journal on Image and Video Processing

ISSN

1687-5176

e-ISSN

1687-5281

Svazek periodika

2025

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

21

Strana od-do

1-21

Kód UT WoS článku

001491533700001

EID výsledku v databázi Scopus

2-s2.0-105005509899