mlsanityData to 7 Oct 2026

Anatomizing Bias in Facial Analysis

arXiv 2112.06522 ↗First posted 13 Dec 2021cs.CVcited by 18

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Abstract (arXiv)

Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overview of existing bias mitigation algorithms. We also discuss open challenges in the field of biased facial analysis.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 39 of its 77 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

39 of 77 references.

References · 39

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  2. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 426
  3. 2018Learning Not to Learn: Training Deep Neural Networks with Biased Datacited by 255
  4. 2019Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigationcited by 252
  5. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
  6. 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
  7. 2019Fair Generative Modeling via Weak Supervisioncited by 114
  8. 2021Fair Mixup: Fairness via Interpolationcited by 109
  9. 2021EnD: Entangling and Disentangling deep representations for bias correctioncited by 105
  10. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  11. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  12. 2018Deep Imbalanced Learning for Face Recognition and Attribute Predictioncited by 90
27 more
  1. 2020Face Recognition: Too Bias, or Not Too Bias?cited by 82
  2. 2019Image Counterfactual Sensitivity Analysis for Detecting Unintended Biascited by 74
  3. 2020Mitigating Face Recognition Bias via Group Adaptive Classifiercited by 67
  4. 2021Fair Feature Distillation for Visual Recognitioncited by 59
  5. 2020SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learningcited by 50
  6. 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
  7. 2020Improving the Fairness of Deep Generative Models without Retrainingcited by 46
  8. 2021Discover the Unknown Biased Attribute of an Image Classifiercited by 46
  9. 2020Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentationcited by 45
  10. 2019Characterizing the Variability in Face Recognition Accuracy Relative to Racecited by 40
  11. 2021Consistent Instance False Positive Improves Fairness in Face Recognitioncited by 40
  12. 2020Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulationcited by 36
  13. 2020Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognitioncited by 31
  14. 2020InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometricscited by 31
  15. 2020Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalizationcited by 28
  16. 2019Deep Learning for Face Recognition: Pride or Prejudiced?cited by 25
  17. 2020Learning Unbiased Representations via Mutual Information Backpropagationcited by 25
  18. 2020Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groupscited by 24
  19. 2019Characterizing Bias in Classifiers using Generative Modelscited by 23
  20. 2020Beyond Identity: What Information Is Stored in Biometric Face Templates?cited by 23
  21. 2021Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasetscited by 22
  22. 2019Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometricscited by 19
  23. 2020Uncovering the Bias in Facial Expressionscited by 12
  24. 2020Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognitioncited by 11
  25. 2021Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Modelscited by 11
  26. 2021IFBiD: Inference-Free Bias Detectioncited by 11
  27. 2020Reducing Geographic Performance Differential for Face Recognitioncited by 4

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Citing papers per year: 2021: 1; 2022: 9; 2023: 2; 2024: 1; 2025: 5.

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  1. 2022A Survey of Machine Unlearningcited by 298
  2. 2022GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detectioncited by 32
  3. 2022Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representationscited by 16
  4. 2022Learning to Split for Automatic Bias Detectioncited by 16
  5. 2022Fairness in Face Presentation Attack Detectioncited by 13
  6. 2022An Examination of Bias of Facial Analysis based BMI Prediction Modelscited by 10
  7. 2022Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groupscited by 7
  8. 2025Review of Demographic Fairness in Face Recognitioncited by 7
  9. 2021Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillationcited by 6
  10. 2025Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Modelscited by 6
  11. 2022Robustness Disparities in Face Detectioncited by 5
  12. 2023Zero-shot racially balanced dataset generation using an existing biased StyleGAN2cited by 5
6 more
  1. 2024Can the accuracy bias by facial hairstyle be reduced through balancing the training data?cited by 5
  2. 2022Are Face Detection Models Biased?cited by 3
  3. 2023On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Normscited by 2
  4. 2025A Feature-level Bias Evaluation Framework for Facial Expression Recognition Modelscited by 0
  5. 2025Auditing and Mitigating Bias in Gender Classification Algorithms: A Data-Centric Approachcited by 0
  6. 2025Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classificationcited by 0