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  4. CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability
 
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2023
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

Title Supplement
Published on arXiv
Abstract
Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms.
Author(s)
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fang, Meiling  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Klemt, Marcel  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fu, Biying  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Hessisches Ministerium für Wissenschaft und Kunst -HMWK-  
Conference
Conference on Computer Vision and Pattern Recognition 2023  
DOI
10.48550/arXiv.2112.06592
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Information Technology

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: Machine learning (ML)

  • LTA: Interactive decision-making support and assistance systems

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • LTA: Generation, capture, processing, and output of images and 3D models

  • Face recognition

  • Biometrics

  • Deep learning

  • Machine learning

  • Quality estimation

  • ATHENE

  • CRISP

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