mlsanityData to 7 Oct 2026

Demographic Fairness in Biometric Systems: What do the Experts say?

arXiv 2105.14844 ↗First posted 31 May 2021cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

Algorithmic decision systems have frequently been labelled as "biased", "racist", "sexist", or "unfair" by numerous media outlets, organisations, and researchers. There is an ongoing debate whether such assessments are justified and whether citizens and policymakers should be concerned. These and other related matters have recently become a hot topic in the context of biometric technologies, which are ubiquitous in personal, commercial, and governmental applications. Biometrics represent an essential component of many surveillance, access control, and operational identity management systems, thus directly or indirectly affecting billions of people all around the world. In order to provide a forum for experts in the field, the European Association for Biometrics organised an event series with "demographic fairness in biometric systems" as an overarching theme. The events featured presentations by international experts from academic, industry, and governmental organisations and facilitated interactions and discussions between the experts and the audience. Further consultation of experts was undertaken by means of a questionnaire. This work summarises opinions of experts and findings of said events on the topic of demographic fairness in biometric systems including several important aspects such as the developments of evaluation metrics and standards as well as related issues, e.g. the need for transparency and explainability in biometric systems or legal and ethical issues.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 78 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

20 of 78 references.

References · 20

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2017Algorithmic decision making and the cost of fairnesscited by 402
  6. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  7. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  8. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  9. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  10. 2021A Comprehensive Study on Face Recognition Biases Beyond Demographicscited by 86
  11. 2019Mitigating Information Leakage in Image Representations: A Maximum Entropy Approachcited by 48
  12. 2020Fairness in Biometrics: a figure of merit to assess biometric verification systemscited by 41
8 more
  1. 2021Biometrics: Trust, but Verifycited by 39
  2. 2020How Does Gender Balance In Training Data Affect Face Recognition Accuracy?cited by 37
  3. 2020This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Modelscited by 33
  4. 2020Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalizationcited by 28
  5. 2019Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometricscited by 19
  6. 2021On the Applicability of Synthetic Data for Face Recognitioncited by 15
  7. 2017An End-To-End Machine Learning Pipeline That Ensures Fairness Policiescited by 3
  8. 2020Demographic Bias: A Challenge for Fingervein Recognition Systems?cited by 2

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 1; 2024: 5; 2025: 2.

12 papers.

  1. 2022Fairness in Face Presentation Attack Detectioncited by 13
  2. 2022How to Boost Face Recognition with StyleGAN?cited by 10
  3. 2024Synthetic Data for the Mitigation of Demographic Biases in Face Recognitioncited by 10
  4. 2022Towards Explaining Demographic Bias through the Eyes of Face Recognition Modelscited by 7
  5. 2025Review of Demographic Fairness in Face Recognitioncited by 7
  6. 2023Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluationcited by 5
  7. 2024The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognitioncited by 3
  8. 2024Fairness measures for biometric quality assessmentcited by 1
  9. 2022Fairness on Synthetic Visual and Thermal Mask Imagescited by 0
  10. 2024Testing the Performance of Face Recognition for People with Down Syndromecited by 0
  11. 2024Improving speaker verification robustness with synthetic emotional utterancescited by 0
  12. 2025Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encodingcited by 0