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  4. Face Image Quality Assessment: A Literature Survey
 
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2022
Journal Article
Title

Face Image Quality Assessment: A Literature Survey

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.
Author(s)
Schlett, Torsten
Hochschule Darmstadt
Rathgeb, Christian
Hochschule Darmstadt
Henniger, Olaf  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Galbally, Javier
European Commission, Joint Research Center
Fierrez, Julian
Univ. Autonoma de Madrid
Busch, Christoph
Hochschule Darmstadt
Journal
ACM Computing Surveys  
Project(s)
BIBECA
TReSPAsS-ETN
Funder
European Commission EC  
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
DOI
10.1145/3507901
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Smart City

  • Research Line: Human computer interaction (HCI)

  • biometrics

  • face recognition

  • image quality

  • ATHENE

  • CRISP

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