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Cited by 15

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Face Recognition in Children: A Longitudinal Study

Bahmani, Keivan, Schuckers, Stephanie·2022·2022 International Workshop on Biometrics and Forensics (IWBF)
DOI 10.1109/iwbf55382.2022.9794551
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Crossref · 10.1109/iwbf55382.2022.9794551

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2 of 2 references verified0 citation errors · 0 not checkable

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  • Bahmani et al. achieved 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 months age gaps with MagFace.

    “With MagFace they achieved 98.3% and 94.9% TAR at 0.1% FAR over 6 and 36 months age gaps.”

    From Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age (2024) · cites this paper as [2]

    Table III (C. Face Detection and Recognition Models) reports MagFace TAR at 0.1% FAR as 98.2% at 6 months, 98.3% at 12 months, and 94.9% at 36 months. The 36-month figure matches the claim, but the claimed 6-month figure belongs to the 12-month age gap. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • Bahmani et al. found a significant and consistent decline in match scores with increasing age gaps between gallery and probe images in children, even over short intervals such as 6 months.

    “The research findings reveal a significant and consistent decline in match scores, with increasing age gaps between gallery and probe images in children, even over short intervals such as 6 months.”

    From Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age (2024) · cites this paper as [2]

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Citations

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Research works printed but not linked: 24

  1. 10.1016/j.patrec.2017.03.006 · DOI
  2. ricanek, 2008, Craniofacial aging, Wiley Handbook of Science and Technology for Homeland Security, 1
  3. 10.1109/CVPR.2006.187 · DOI · Paper page
  4. ricanek, 2015, A review of face recognition against longitudinal child faces, BIOSIG 2015
  5. 10.1109/CVPRW.2019.00280 · DOI
  6. 10.1109/CVPR.2015.7298682 · DOI · Paper page
  7. guo, 2016, Ms-celeb-1m: A dataset and benchmark for large-scale face recognition, European Conference on Computer Vision, 87
  8. 10.1109/ICB2018.2018.00041 · DOI
  9. 10.1109/CVPR.2019.00482 · DOI · Paper page
  10. 10.1109/TBIOM.2021.3050094 · DOI
  11. 10.1109/FGR.2006.78 · DOI · Paper page
  12. 10.1007/s11263-018-01142-4 · DOI
  13. 10.1007/978-3-642-33868-7_19 · DOI
  14. 10.6028/NIST.IR.8271 · DOI
  15. seabold, 2010, Statsmodels: Econometric and statistical modeling with python, Proceedings of the 9th Python in Science Conference, 57, 61
  16. huang, 2014, Labeled faces in the wild: Updates and new reporting procedures, Dept Comput Sci Univ Massachusetts Amherst Amherst MA USA Tech Rep, 14
  17. 10.1109/TMM.2015.2420374 · DOI
  18. 10.1504/IJBM.2010.030415 · DOI · Paper page
  19. 10.1109/CVPRW.2017.250 · DOI
  20. keahey, 2020, Lessons learned from the chameleon testbed, Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), 219
  21. 10.5244/C.29.41 · DOI · Paper page
  22. 10.1109/CVPR46437.2021.01400 · DOI
  23. 10.1109/LSP.2016.2603342 · DOI · Paper page
  24. lindstrom, 1988, Newton—Raphson and EM algorithms for linear mixed-effects models for repeated-measures data, Journal of the American Statistical Association, 83, 1014

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Metadata

Authors
Bahmani, K., & Schuckers, S.
Venue
2022 International Workshop on Biometrics and Forensics (IWBF)
Publisher
IEEE
Published
2022-04-20
Pages
1-6
DOI
10.1109/iwbf55382.2022.9794551
Cited by
15
References
2
Licence
Funders
National Science Foundation

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