mlsanityData to 14 Oct 2026

Demographic Fairness in Face Identification: The Watchlist Imbalance Effect

arXiv 2106.08049 ↗First posted 15 Jun 2021cs.CV cs.CYcited by 1

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Abstract (arXiv)

Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect is referred to as "watchlist imbalance effect". In this work, we present a method to theoretically estimate said effect for a biometric identification system given its verification performance across demographic groups and the composition of the used gallery. Further, we report results for identification experiments on differently composed demographic subsets, i.e. females and males, of the public academic MORPH database using the open-source ArcFace face recognition system. It is shown that the database composition has a huge impact on performance differentials in biometric identification systems, even if performance differentials are less pronounced in the verification scenario. This study represents the first detailed analysis of the watchlist imbalance effect which is expected to be of high interest for future research in the field of facial recognition.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 15 Jun 2021.

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6 of 22 references.

References · 6

  1. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  2. 2019FairFace: Face Attribute Dataset for Balanced Race, Gender, and Agecited by 87
  3. 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
  4. 2020Fairness in Biometrics: a figure of merit to assess biometric verification systemscited by 41
  5. 2020Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Arecited by 23
  6. 2019Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometricscited by 19

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Citing papers per year: 2022: 1.

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