mlsanityData to 14 Oct 2026

A Deep Dive into Dataset Imbalance and Bias in Face Identification

arXiv 2203.08235 ↗First posted 15 Mar 2022cs.CV cs.LGcited by 11

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Abstract (arXiv)

As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e., that FR models perform worse on images of non-white people or women because these demographic groups are underrepresented in training data. Recent academic research paints a more nuanced picture of this relationship. However, previous studies of data imbalance in FR have focused exclusively on the face verification setting, while the face identification setting has been largely ignored, despite being deployed in sensitive applications such as law enforcement. This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity. In this work, we address this gap in the research by thoroughly exploring the effects of each kind of imbalance possible in face identification, and discuss other factors which may impact bias in this setting.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

17 of 43 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  4. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  5. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
  6. 2018MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devicescited by 182
  7. 2015Cost Sensitive Learning of Deep Feature Representations from Imbalanced Datacited by 167
  8. 2014Deep Learning Face Representation by Joint Identification-Verificationcited by 162
  9. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
  10. 2018Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddingscited by 114
  11. 2020Face Recognition: Too Bias, or Not Too Bias?cited by 82
  12. 2019Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?cited by 56
5 more
  1. 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
  2. 2017Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systemscited by 28
  3. 2021Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasetscited by 22
  4. 2021Robustness Disparities in Commercial Face Detectioncited by 11
  5. 2021Comparing Human and Machine Bias in Face Recognitioncited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 6; 2026: 1.

11 papers.

  1. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
  2. 2024Synthetic Data for the Mitigation of Demographic Biases in Face Recognitioncited by 10
  3. 2023Fairer and More Accurate Tabular Models Through NAScited by 3
  4. 2025Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Imagescited by 1
  5. 2025Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Biascited by 1
  6. 2022How Technology Impacts and Compares to Humans in Socially Consequential Arenascited by 0
  7. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  8. 2025Exploring Disparity-Accuracy Trade-offs in Face Recognition Systems: The Role of Datasets, Architectures, and Loss Functionscited by 0
  9. 2025Pura: An Efficient Privacy-Preserving Solution for Face Recognitioncited by 0
  10. 2025A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissancecited by 0
  11. 2026FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probingcited by 0