mlsanityData to 7 Oct 2026

About Face: A Survey of Facial Recognition Evaluation

arXiv 2102.00813 ↗First posted 1 Feb 2021cs.CV cs.AIcited by 27

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Abstract (arXiv)

We survey over 100 face datasets constructed between 1976 to 2019 of 145 million images of over 17 million subjects from a range of sources, demographics and conditions. Our historical survey reveals that these datasets are contextually informed, shaped by changes in political motivations, technological capability and current norms. We discuss how such influences mask specific practices (some of which may actually be harmful or otherwise problematic) and make a case for the explicit communication of such details in order to establish a more grounded understanding of the technology's function in the real world.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 19 of its 76 references that are in the map. First posted on arXiv 1 Feb 2021.

Builds on

19 of 76 references.

References · 19

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  2. 2018Datasheets for Datasetscited by 1,371
  3. 2018Model Cards for Model Reportingcited by 1,068
  4. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  5. 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 685
  6. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 621
  7. 2014Learning Face Representation from Scratchcited by 451
  8. 2018Deep Face Recognition: A Surveycited by 275
  9. 2015The MegaFace Benchmark: 1 Million Faces for Recognition at Scalecited by 199
  10. 2015End-to-end people detection in crowded scenescited by 140
  11. 2019Diversity in Facescited by 77
  12. 2018Racial categories in machine learningcited by 48
7 more
  1. 2017Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approachcited by 43
  2. 2019Characterizing the Variability in Face Recognition Accuracy Relative to Racecited by 40
  3. 2019SensitiveNets: Learning Agnostic Representations with Application to Face Imagescited by 17
  4. 2016Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)cited by 14
  5. 2013Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognitioncited by 13
  6. 2018Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Networkcited by 7
  7. 2019A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Imagescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 9; 2023: 6; 2024: 8; 2025: 1; 2026: 1.

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  1. 2022When and why vision-language models behave like bags-of-words, and what to do about it?cited by 603
  2. 2021Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implicationscited by 128
  3. 2022SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Datacited by 63
  4. 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
  5. 2022SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Datacited by 30
  6. 2023Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognitioncited by 27
  7. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
  8. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  9. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  10. 2022Bias in Automated Speaker Recognitioncited by 20
  11. 2022QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantizationcited by 18
  12. 2023Ethical Considerations for Responsible Data Curationcited by 17
15 more
  1. 2022Simplicity Bias Leads to Amplified Performance Disparitiescited by 11
  2. 2024Benchmark Data Repositories for Better Benchmarkingcited by 10
  3. 2022Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPAcited by 5
  4. 2024A Survey of RWKVcited by 5
  5. 2023The Surveillance AI Pipelinecited by 3
  6. 2024Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verificationcited by 2
  7. 2022CarFi: Rider Localization Using Wi-Fi CSIcited by 1
  8. 2023Embedding Aggregation for Forensic Facial Comparisoncited by 0
  9. 2023Deep Learning and Ethicscited by 0
  10. 2023CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformitycited by 0
  11. 2024Mask-up: Investigating Biases in Face Re-identification for Masked Facescited by 0
  12. 2024IPFed: Identity protected federated learning for user authenticationcited by 0
  13. 2024Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasetscited by 0
  14. 2025Reliable and Reproducible Demographic Inference for Fairness in Face Analysiscited by 0
  15. 2026A survey of facial recognition techniquescited by 0