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Demographic Variability in Face Image Quality Measures

arXiv 2501.07898 ↗First posted 14 Jan 2025cs.CVcited by 0

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Abstract (arXiv)

Face image quality assessment (FIQA) algorithms are being integrated into online identity management applications. These applications allow users to upload a face image as part of their document issuance process, where the image is then run through a quality assessment process to make sure it meets the quality and compliance requirements. Concerns about demographic bias have been raised about biometric systems, given the societal implications this may cause. It is therefore important that demographic variability in FIQA algorithms is assessed such that mitigation measures can be created. In this work, we study the demographic variability of all face image quality measures included in the ISO/IEC 29794-5 international standard across three demographic variables: age, gender, and skin tone. The results are rather promising and show no clear bias toward any specific demographic group for most measures. Only two quality measures are found to have considerable variations in their outcomes for different groups on the skin tone variable.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 14 Jan 2025.

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References · 7

  1. 2020Face Image Quality Assessment: A Literature Surveycited by 79
  2. 2022Fair Contrastive Learning for Facial Attribute Classificationcited by 71
  3. 2023Consensus and Subjectivity of Skin Tone Annotation for ML Fairnesscited by 22
  4. 2023Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Colorcited by 17
  5. 2021Reliability and Validity of Image-Based and Self-Reported Skin Phenotype Metricscited by 15
  6. 2019Some Research Problems in Biometrics: The Future Beckonscited by 13
  7. 2017AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Facescited by 11

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