mlsanityData to 14 Oct 2026

Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color

arXiv 2309.05148 ↗First posted 10 Sep 2023cs.CVcited by 17

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Abstract (arXiv)

This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have shown how gender classification systems can be biased against women with darker skin tones. Subsequently, fairness researchers and practitioners have adopted the Fitzpatrick skin type classification as a common measure to assess skin color bias in computer vision systems. While effective, the Fitzpatrick scale only focuses on the skin tone ranging from light to dark. Towards a more comprehensive measure of skin color, we introduce the hue angle ranging from red to yellow. When applied to images, the hue dimension reveals additional biases related to skin color in both computer vision datasets and models. We then recommend multidimensional skin color scales, relying on both skin tone and hue, for fairness assessments.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 32 of its 84 references that are in the map. First posted on arXiv 10 Sep 2023.

Builds on

32 of 84 references.

References · 32

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  4. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  5. 2018Datasheets for Datasetscited by 1,386
  6. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  7. 2018Model Cards for Model Reportingcited by 1,080
  8. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 695
  9. 2019MaskGAN: Towards Diverse and Interactive Facial Image Manipulationcited by 628
  10. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 544
  11. 2022Perception Prioritized Training of Diffusion Modelscited by 303
  12. 2019Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigationcited by 252
20 more
  1. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 134
  2. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 125
  3. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  4. 2021Understanding and Evaluating Racial Biases in Image Captioningcited by 108
  5. 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
  6. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  7. 2019Diversity in Facescited by 77
  8. 2019Predictive Inequity in Object Detectioncited by 77
  9. 2022Fair Contrastive Learning for Facial Attribute Classificationcited by 71
  10. 2020Towards causal benchmarking of bias in face analysis algorithmscited by 64
  11. 2020Lifespan Age Transformation Synthesiscited by 60
  12. 2020"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairnesscited by 56
  13. 2022Third Time's the Charm? Image and Video Editing with StyleGAN3cited by 54
  14. 2016Image Aesthetic Assessment: An Experimental Surveycited by 50
  15. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  16. 2022Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguationcited by 32
  17. 2021Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agencycited by 22
  18. 2023Consensus and Subjectivity of Skin Tone Annotation for ML Fairnesscited by 22
  19. 2021Feature and Label Embedding Spaces Matter in Addressing Image Classifier Biascited by 11
  20. 2021Two-Face: Adversarial Audit of Commercial Face Recognition Systemscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 2.

17 papers.

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  1. 2024AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmarkcited by 28
  2. 2024VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognitioncited by 8
  3. 2025Bias Amplification in Stable Diffusion's Representation of Stigma Through Skin Tones and Their Homogeneitycited by 5
  4. 2025Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tonecited by 4
  5. 2024PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flowscited by 3
  6. 2024Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendationscited by 3
  7. 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
  8. 2025TrueSkin: Towards Fair and Accurate Skin Tone Recognition and Generationcited by 2
  9. 2025Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regressioncited by 1
  10. 2026Neutral Prompts, Non-Neutral People: Quantifying Gender and Skin-Tone Bias in Gemini Flash 2.5 Image and GPT Image 1.5cited by 1
  11. 2025Demographic Variability in Face Image Quality Measurescited by 0
  12. 2025Robustness and sex differences in skin cancer detection: logistic regression vs CNNscited by 0
5 more
  1. 2025Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generationcited by 0
  2. 2025Geometry-Aware Texture Generation for 3D Head Modeling with Artist-driven Controlcited by 0
  3. 2025TrustSkin: A Fairness Pipeline for Trustworthy Facial Affect Analysis Across Skin Tonecited by 0
  4. 2025Reliable and Reproducible Demographic Inference for Fairness in Face Analysiscited by 0
  5. 2026True to Tone? Quantifying Skin Tone Fidelity and Bias in Photographic-to-Virtual Human Pipelinescited by 0