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Current face recognition systems achieve high progress on several benchmark tests.
Algorithm as 136: A k-means clustering algorithm
J. A. Hartigan and M. A. Wong · 1979
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The feret evaluation methodology for face-recognition algorithms
P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss · 2000
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Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis
N. Furl, P. J. Phillips, and A. J. O’Toole · 2002
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Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2002
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MORPH: A longitudinal image database of normal adult age-progression
K. R. Jr. and T. Tesafaye · 2006
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Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
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Dlib-ml: A machine learning toolkit
D. E. King · 2009
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An other-race effect for face recognition algorithms
P. J. Phillips, F. Jiang, A. Narvekar, J. Ayyad, and A. J. O’Toole · 2011
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Age and gender estimation of unfiltered faces
E. Eidinger, R. Enbar, and T. Hassner · 2014
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Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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The Perpetual Line-up: Unregulated Police Face Recognition in America
C. Garvie, G. U. C. on Privacy, Technology, and G. U. L. C. C. on Privacy & Technology · 2016
Cited alongside, same era.
Eu regulations on algorithmic decision-making and a ”right to explanation”, 2016
B. Goodman and S. Flaxman · 2016
Cited alongside, same era.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
Cited alongside, same era.
50 years of biometric research: Accomplishments, challenges, and opportunities
A. K. Jain, K. Nandakumar, and A. Ross · 2016
Cited alongside, same era.
Are face recognition systems accurate? depends on your race
M. Orcutt · 2016
Cited alongside, same era.
Analyzing and reducing the damage of dataset bias to face recognition with synthetic data
A. Kortylewski, B. Egger, A. Schneider, T. Gerig, A. Morel-Forster, and T. Vetter · 2019
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Additive adversarial learning for unbiased authentication
J. Liang, Y. Cao, C. Zhang, S. Chang, K. Bai, and Z. Xu · 2019
Later among the works it cites.
Face recognition algorithm bias: Performance differences on images of children and adults
N. Srinivas, K. Ricanek, D. Michalski, D. S. Bolme, and M. King · 2019
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Multi-algorithmic fusion for reliable age and gender estimation from face images
P. Terhörst, M. Huber, J. N. K. N. Damer, F. Kirchbuchner, and A. Kuijper · 2019
Later among the works it cites.
Reliable age and gender estimation from face images: Stating the confidence of model predictions
P. Terhörst, M. Huber, J. N. Kolf, I. Zelch, N. Damer, F. Kirchbuchner, and A. Kuijper · 2019
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Best practice technical guidelines for automated border control (abc) systems
Frontex · 2017
Cited alongside, same era.
The EU General Data Protection Regulation (GDPR): A Practical Guide
P. Voigt and A. v. d. Bussche · 2017
Cited alongside, same era.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
M. S. Alvi, A. Zisserman, and C. Nellåker · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
Cited alongside, same era.
Mitigating bias in gender, age and ethnicity classification: A multi-task convolution neural network approach
A. Das, A. Dantcheva, and F. Bremond · 2018
Cited alongside, same era.
Deep imbalanced learning for face recognition and attribute prediction
C. Huang, Y. Li, C. C. Loy, and X. Tang · 2018
Cited alongside, same era.
DebFace: De-biasing face recognition
S. Gong, X. Liu, and A. K. Jain · 2019
Cited alongside, same era.
M. Wang and W. Deng · 2019
Later among the works it cites.
Deep class-skewed learning for face recognition
P. Wang, F. Su, Z. Zhao, Y. Guo, Y. Zhao, and B. Zhuang · 2019
Later among the works it cites.
Feature transfer learning for face recognition with under-represented data
X. Yin, X. Yu, K. Sohn, X. Liu, and M. Chandraker · 2019
Later among the works it cites.
Demographic bias in biometrics: A survey on an emerging challenge
P. Drozdowski, C. Rathgeb, A. Dantcheva, N. Damer, and C. Busch · 2020
Closest in time.
Demographic bias in presentation attack detection of iris recognition systems
M. Fang, N. Damer, F. Kirchbuchner, and A. Kuijper · 2020
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Beyond identity: What information is stored in biometric face templates?
P. Terhörst, D. Fährmann, N. Damer, F. Kirchbuchner, and A. Kuijper · 2020
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P. Terhörst, J. N. Kolf, N. Damer, F. Kirchbuchner, and A. Kuijper · 2020
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Comparison-level mitigation of ethnic bias in face recognition
P. Terhörst, M. L. Tran, N. Damer, F. Kirchbuchner, and A. Kuijper · 2020
Closest in time.