Face Recognition Bias Assessment through Quality Estimation Models
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http://hdl.handle.net/10045/138773
Títol: | Face Recognition Bias Assessment through Quality Estimation Models |
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Autors: | López Payá, Luis | Cordoba, Pedro | Sánchez Pérez, Ángela | Barrachina, Javier | Benavent-Lledó, Manuel | Mulero Pérez, David | Garcia-Rodriguez, Jose |
Grups d'investigació o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Quality | Bias | Face recognition | Deep learning |
Data de publicació: | 15-de novembre-2023 |
Editor: | MDPI |
Citació bibliogràfica: | Lopez Paya L, Cordoba P, Sanchez Perez A, Barrachina J, Benavent-Lledo M, Mulero-Pérez D, Garcia-Rodriguez J. Face Recognition Bias Assessment through Quality Estimation Models. Electronics. 2023; 12(22):4649. https://doi.org/10.3390/electronics12224649 |
Resum: | Recent advances in facial recognition technology have achieved outstanding performance, but unconstrained face recognition remains an ongoing issue. Facial-image-quality-evaluation algorithms evaluate the quality of the input samples, providing crucial information about the accuracy of recognition decisions. By doing so, this can lead to improved results in challenging scenarios. In recent years, significant progress has been made in assessing the quality of facial images. The computation of quality scores has become highly precise and closely correlated with the model results. In this paper, we reviewed and analyzed the existing biases of cutting-edge quality-estimation techniques for face recognition. Our experimentation focused on the quality estimators developed by MagFace, FaceQNet, and SER-FIQ and were evaluated on the CelebA reference dataset. A study of bias in the face-recognition model was conducted by analyzing the quality scores presented in each article. This allowed for an examination of existing biases within both the quality estimators and the face-recognition models. |
Patrocinadors: | We would like to thank the “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the TED2021-130890B (CHAN-TWIN) research Project funded by MCIN/AEI/10.13039554/501100011033 and the European Union NextGenerationEU/PRTR; HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning; CIAICO/2022/132 Consolidated group project “AI4Health” funded by the Valencian government and International Center for Aging Research ICAR funded project “IASISTEM”. This work was also supported by two Spanish national and regional grants for Ph.D. studies, FPU21/00414 and CIACIF/2021/430. |
URI: | http://hdl.handle.net/10045/138773 |
ISSN: | 2079-9292 |
DOI: | 10.3390/electronics12224649 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/electronics12224649 |
Apareix a la col·lecció: | INV - AIA - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Lopez-Paya_etal_2023_Electronics.pdf | 2,84 MB | Adobe PDF | Obrir Vista prèvia | |
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