Face image quality assessment: a literature survey
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
ACMDate
2022-01-05Citation
10.1145/3507901
ACM Computing Surveys 54 (2022): 210
ISSN
0360-0300DOI
10.1145/3507901Funded by
This research work has been funded by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE, project BIBECA (RTI2018-101248-B-I00 MINECO/FEDER), and project TReSPAsS-ETN (H2020- MSCA-ITN-2019-860813). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883356. This text reflects only the authors’ views and the Commission is not liable for any use that may be made of the information contained thereinProject
info:eu-repo/grantAgreement/EC/H2020/860813; info:eu-repo/grantAgreement/EC/H2020/883356; Gobierno de España. RTI2018-101248-B-I00Editor's Version
https://doi.org/10.1145/3507901Subjects
Biometric sample quality; Face image quality assessment; Face recognition; TelecomunicacionesRights
© 2022 Copyright held by the owner/author(s).Abstract
The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning-based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a., highlighting the importance of comparability for algorithm evaluations and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions
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Google Scholar:Schlett, Torsten
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Rathgeb, Christian
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Henninger, Olaf
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Galbally, Javier
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Fiérrez Aguilar, Julián
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Busch, Christoph
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