mlsanityData to 7 Oct 2026

Exploring Causes of Demographic Variations In Face Recognition Accuracy

arXiv 2304.07175 ↗First posted 14 Apr 2023cs.CVcited by 1

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Abstract (arXiv)

In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cross-demographic performance. We consider accuracy differences as represented by variations in non-mated (impostor) and / or mated (genuine) distributions for 1-to-1 face matching. Possible causes explored include differences in skin tone, face size and shape, imbalance in number of identities and images in the training data, and amount of face visible in the test data ("face pixels"). We find that demographic differences in face pixel information of the test images appear to most directly impact the resultant differences in face recognition accuracy.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

13 of 42 references.

References · 13

  1. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  2. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  3. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 621
  4. 2020Partial FC: Training 10 Million Identities on a Single Machinecited by 163
  5. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  6. 2019FairFace: Face Attribute Dataset for Balanced Race, Gender, and Agecited by 87
  7. 2019Diversity in Facescited by 77
  8. 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
  9. 2019Characterizing the Variability in Face Recognition Accuracy Relative to Racecited by 40
  10. 2020How Does Gender Balance In Training Data Affect Face Recognition Accuracy?cited by 37
  11. 2021Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphologycited by 32
  12. 2021Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applicationscited by 16
1 more
  1. 2021Reliability and Validity of Image-Based and Self-Reported Skin Phenotype Metricscited by 15

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Review of Demographic Fairness in Face Recognitioncited by 7