Cited by 42Face recognition and analysisEthics and Social Impacts of AIFace and Expression Recognition

Investigating bias in deep face analysis: The KANFace dataset and empirical study

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Georgopoulos, Markos, Panagakis, Yannis, Pantic, Maja·2020·Image and Vision Computing

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  • Generation of synthetic data by scaling, rotating, and orientation to meet training sample requirements leads to biased results due to less diversified datasets.

    “The generation of synthetic data by scaling, rotating, and orientation to meet the requirement of the training sample data leads to biased results due to less diversified datasets [48].”

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Research works printed but not linked: 3

  1. I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, E. Brossard, The megaface benchmark: 1 million faces for recognition at scale, in: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, 2016, pp. 4873-4882.
  2. A. Bansal, C. Castillo, R. Ranjan, R. Chellappa, The dos and donts for cnn-based face verification, in: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), IEEE, 2017, pp. 2545-2554.
  3. L. v. d. Maaten, G. Hinton, Visualizing data using t-sne, Journal of ma- chine learning research 9 (Nov) (2008) 2579-2605.

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Authors
Georgopoulos, M., Panagakis, Y., & Pantic, M.
Affiliations
  • Imperial College London (GB)
  • National and Kapodistrian University of Athens (GR)
Corresponding author
Georgopoulos, Markos
Venue
Image and Vision Computing
Publisher
Elsevier BV
Published
2020-10
Volume
102
Pages
103954
ISSN
0262-8856 (ISSN-L)
OpenAlex
W3024292796
Cited by
42
References
56
Citations by year
2025: 10 · 2024: 6 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 5
FWCI
2.53
Type
journal article
Language
en
Open access
Locations
  • Image and Vision Computing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Engineering and Physical Sciences Research Council
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords
demographic bias, face recognition, age estimation, gender classification, kinship verification, algorithmic fairness

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