LossFIQA: A Shortcut Solution to Image Quality Assessment Using Loss for Faces and Beyond

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0198680" target="_blank" >RIV/00216305:26230/26:0198680 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11082134" target="_blank" >https://ieeexplore.ieee.org/document/11082134</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3589778" target="_blank" >10.1109/ACCESS.2025.3589778</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LossFIQA: A Shortcut Solution to Image Quality Assessment Using Loss for Faces and Beyond

Popis výsledku v původním jazyce

We introduce a novel approach to model-based quality assessment of input images. Our approach is very simple, and we demonstrate experimentally that it is not limited to a single domain (typically face recognition in the literature). Our approach generates per-sample quality pseudo-labels directly from the objective function used during the training of the target model. We evaluate the proposed method on eight large and respected datasets (from the face recognition on LFW, CALFW, CPLFW, XQLFW, CFP-FP, AgeDB, IJB-C, and retinopathy detection domain on EyePACS dataset) and using multiple state-of-the-art models (AdaFace, MagFace, ArcFace, ElasticFace, and CuricularFace). Compared to state-of-the-art methods for face quality assessment that are considerably more complex, our solution yields competitive results while being much simpler and not limited to one application.

Název v anglickém jazyce

LossFIQA: A Shortcut Solution to Image Quality Assessment Using Loss for Faces and Beyond

Popis výsledku anglicky

We introduce a novel approach to model-based quality assessment of input images. Our approach is very simple, and we demonstrate experimentally that it is not limited to a single domain (typically face recognition in the literature). Our approach generates per-sample quality pseudo-labels directly from the objective function used during the training of the target model. We evaluate the proposed method on eight large and respected datasets (from the face recognition on LFW, CALFW, CPLFW, XQLFW, CFP-FP, AgeDB, IJB-C, and retinopathy detection domain on EyePACS dataset) and using multiple state-of-the-art models (AdaFace, MagFace, ArcFace, ElasticFace, and CuricularFace). Compared to state-of-the-art methods for face quality assessment that are considerably more complex, our solution yields competitive results while being much simpler and not limited to one application.

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

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

126915-126924

Kód UT WoS článku

001536742300019

EID výsledku v databázi Scopus

2-s2.0-105011055485