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A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups
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Yau et al. demonstrated significant variations in authentication robustness between age groups.
“A study conducted by Yau et al. [18], significant variations in authentication robustness between age groups are demonstrated.”
From Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age (2024) · cites this paper as [18]
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Deep Residual Learning for Image Recognition
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“[28] measure the performances of three commercial face recognition algorithms on three age groups and find that younger subjects (18 to 30 years old) are more difficult to recognize.”
BackgroundResultLongitudinal Study of Child Face Recognition
Deb, D., Nain, N., & Jain, A. K. (2018) · 2018 International Conference on Biometrics (ICB) · doi:10.1109/icb2018.2018.00042 · Cited by 73
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Sorted by citations
Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age
Singh, S., Bahmani, K., & Schuckers, S. (2024) · 2024 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744505 · Cited by 5
Metadata
- Authors
- Yao, W., Farooq, M. A., Lemley, J., & Corcoran, P.
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 97118-97134
- ISSN
- 2169-3536
- Cited by
- 8
- References
- 3
- Funders
- Irish Research Council Enterprise Partnership Ph.D. Scheme (EPSPG/2020/40)
- Data-Center Audio/Visual Intelligence On-Device (DAVID) Project
- Disruptive Technologies Innovation Fund
- Xperi Corporation, Ireland
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