mlsanityData to 13 Oct 2026

Lights, Camera, Matching: The Role of Image Illumination in Fair Face Recognition

arXiv 2501.08910 ↗First posted 15 Jan 2025cs.CVcited by 3

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Abstract (arXiv)

Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap between the similarity score distributions for Caucasian and African American female mated image pairs, as measured by d' between distributions. To balance brightness across demographic groups, we conduct three experiments, interpreting brightness in the face skin region either as median pixel value or as the distribution of pixel values. Balancing based on median brightness alone yields up to a 46.8% decrease in d', while balancing based on brightness distribution yields up to a 57.6% decrease. In all three cases, the similarity scores of the individual distributions improve, with mean scores maximally improving 5.9% for Caucasian females and 3.7% for African American females.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 15 Jan 2025.

Builds on

13 of 26 references.

References · 13

  1. 2020Partial FC: Training 10 Million Identities on a Single Machinecited by 163
  2. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  3. 2020Mitigating Face Recognition Bias via Group Adaptive Classifiercited by 67
  4. 2019Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?cited by 56
  5. 2019Characterizing the Variability in Face Recognition Accuracy Relative to Racecited by 40
  6. 2020How Does Gender Balance In Training Data Affect Face Recognition Accuracy?cited by 37
  7. 2021Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphologycited by 32
  8. 2020Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalizationcited by 28
  9. 2022Face Recognition Accuracy Across Demographics: Shining a Light Into the Problemcited by 25
  10. 2022The Gender Gap in Face Recognition Accuracy Is a Hairy Problemcited by 15
  11. 2021Pixel-Level Face Image Quality Assessment for Explainable Face Recognitioncited by 14
  12. 2023What Should Be Balanced in a "Balanced" Face Recognition Dataset?cited by 13
1 more
  1. 2024Can the accuracy bias by facial hairstyle be reduced through balancing the training data?cited by 5

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.