mlsanityData to 7 Oct 2026

Fairness measures for biometric quality assessment

arXiv 2408.11392 ↗First posted 21 Aug 2024cs.CVcited by 1

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Abstract (arXiv)

Quality assessment algorithms measure the quality of a captured biometric sample. Since the sample quality strongly affects the recognition performance of a biometric system, it is essential to only process samples of sufficient quality and discard samples of low-quality. Even though quality assessment algorithms are not intended to yield very different quality scores across demographic groups, quality score discrepancies are possible, resulting in different discard ratios. To ensure that quality assessment algorithms do not take demographic characteristics into account when assessing sample quality and consequently to ensure that the quality algorithms perform equally for all individuals, it is crucial to develop a fairness measure. In this work we propose and compare multiple fairness measures for evaluating quality components across demographic groups. Proposed measures, could be used as potential candidates for an upcoming standard in this important field.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

10 of 16 references.

References · 10

  1. 2021MagFace: A Universal Representation for Face Recognition and Quality Assessmentcited by 332
  2. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  3. 2020Face Image Quality Assessment: A Literature Surveycited by 79
  4. 2021CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiabilitycited by 68
  5. 2021SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distancecited by 63
  6. 2020Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognitioncited by 31
  7. 2022Evaluating Proposed Fairness Models for Face Recognition Algorithmscited by 18
  8. 2021Demographic Fairness in Biometric Systems: What do the Experts say?cited by 12
  9. 2024GraFIQs: Face Image Quality Assessment Using Gradient Magnitudescited by 12
  10. 2022Assessing Bias in Face Image Quality Assessmentcited by 5

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