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Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications.
J. S. B. T. Evans, Bias in human reasoning: Causes and consequences . Lawrence Erlbaum Associates, 1989
1989
Earlier work this paper cites.
B. Friedman and H. Nissenbaum, “Bias in computer systems,” Transactions on Information Systems , vol. 14, no. 3, pp. 330–347, July 1996
1996
Earlier work this paper cites.
K. L. Mosier, L. J. Skitka, S. Heers, and M. Burdick, “Automation bias: Decision making and performance in high-tech cockpits,” The International journal of aviation psychology , vol. 8, no. 1, pp. 47–63, January 1998
1998
Earlier work this paper cites.
G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds, “Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation,” in International Conference on Spoken Language Processing . Australian Speech Science and Technology Association, December 1998, pp. 1351–1355
1998
Earlier work this paper cites.
N. Furl, P. J. Phillips, and A. J. O’Toole, “Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis,” Cognitive Science , vol. 26, no. 6, pp. 797–815, November 2002
2002
Earlier work this paper cites.
R. A. Hicklin and C. L. Reedy, “Implications of the IDENT/IAFIS image quality study for visa fingerprint processing,” Mitretek Systems, Tech. Rep., October 2002
2002
Earlier work this paper cites.
J. Ortega-Garcia, J. Fierrez-Aguilar, D. Simon, J. Gonzalez, M. Faundez-Zanuy et al. , “MCYT baseline corpus: a bimodal biometric database,” IEE Proceedings – Vision, Image and Signal Processing , vol. 150, no. 6, pp. 395–401, December 2003
2003
Earlier work this paper cites.
S. Z. Li and A. K. Jain, Handbook of face recognition . Springer, 2004
2004
Earlier work this paper cites.
P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang et al. , “Overview of the face recognition grand challenge,” in Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , vol. 1. IEEE, June 2005, pp. 947–954
2005
Earlier work this paper cites.
N. C. Sickler and S. J. Elliott, “An evaluation of fingerprint image quality across an elderly population vis-a-vis an 18-25 year old population,” in International Carnahan Conference on Security Technology . IEEE, October 2005, pp. 68–73
2005
Earlier work this paper cites.
M. Arnold, C. Busch, and H. Ihmor, “Investigating performance and impacts on fingerprint recognition systems,” in SMC Information Assurance Workshop . IEEE, June 2005, pp. 1–7
2005
Earlier work this paper cites.
R. R. Banks, J. L. Eberhardt, and L. Ross, “Discrimination and implicit bias in a racially unequal society,” California Law Review , vol. 94, no. 4, pp. 1169–1190, July 2006
2006
Earlier work this paper cites.
——, ISO/IEC 19795-1:2006. Information Technology – Biometric Performance Testing and Reporting – Part 1: Principles and Framework , International Organization for Standardization and International Electrotechnical Committee, April 2006
2006
Earlier work this paper cites.
S. K. Modi and S. J. Elliott, “Impact of image quality on performance: Comparison of young and elderly fingerprints,” in International Conference on Recent Advances in Soft Computing (RASC) , July 2006, pp. 449–454
2006
Earlier work this paper cites.
K. Ricanek and T. Tesafaye, “MORPH: a longitudinal image database of normal adult age-progression,” in International Conference on Automatic Face and Gesture Recognition (FGR) . IEEE, April 2006, pp. 341–345
2006
Earlier work this paper cites.
A. K. Jain, P. Flynn, and A. Ross, Handbook of biometrics . Springer, 2007
2007
Earlier work this paper cites.
N. Yager and T. Dunstone, “Worms, chameleons, phantoms and doves: New additions to the biometric menagerie,” in Workshop on Automatic Identification Advanced Technologies (AutoID) . IEEE, June 2007, pp. 1–6
2007
Earlier work this paper cites.
S. K. Modi, S. J. Elliott, J. Whetsone, and H. Kim, “Impact of age groups on fingerprint recognition performance,” in Workshop on Automatic Identification Advanced Technologies (AutoID) . IEEE, June 2007, pp. 19–23
2007
Earlier work this paper cites.
D. K. Citron, “Technological due process,” Washington University Law Review , vol. 85, 2007
2007
Earlier work this paper cites.
M. Frick, S. K. Modi, S. Elliott, and E. P. Kukula, “Impact of gender on fingerprint recognition systems,” in International Conference on Information Technology and Applications (ICITA) , 2008, pp. 717–721
2008
Earlier work this paper cites.
J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning . Springer, February 2009, vol. 1, no. 10
2009
Earlier work this paper cites.
D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of fingerprint recognition . Springer, 2009
2009
Earlier work this paper cites.
J. R. Beveridge, G. H. Givens, P. J. Phillips, and B. A. Draper, “Factors that influence algorithm performance in the face recognition grand challenge,” Computer Vision and Image Understanding , vol. 113, no. 6, pp. 750–762, June 2009
2009
Earlier work this paper cites.
Y. M. Lui, D. Bolme, B. A. Draper, J. R. Beveridge, G. Givens, and P. J. Phillips, “A meta-analysis of face recognition covariates,” in International Conference on Biometrics: Theory, Applications, and Systems (BTAS) . IEEE, September 2009, pp. 1–8
2009
Earlier work this paper cites.
A. Uhl and P. Wild, “Comparing verification performance of kids and adults for fingerprint, palmprint, hand-geometry and digitprint biometrics,” in International Conference on Biometrics: Theory, Applications, and Systems (BTAS) . IEEE, September 2009, pp. 1–6
2009
Earlier work this paper cites.
R. Parasuraman and D. H. Manzey, “Complacency and bias in human use of automation: An attentional integration,” Human factors , vol. 52, no. 3, pp. 381–410, June 2010
2010
Earlier work this paper cites.
A. Kumar and A. Passi, “Comparison and combination of iris matchers for reliable personal authentication,” Pattern Recognition , vol. 43, no. 3, pp. 1016–1026, March 2010
2010
Earlier work this paper cites.
G. Guo and G. Mu, “Human age estimation: What is the influence across race and gender?” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2010, pp. 71–78
2010
Earlier work this paper cites.
P. Grother, G. W. Quinn, and P. J. Phillips, “Report on the evaluation of 2D still-image face recognition algorithms,” National Institute of Standards and Technology, Tech. Rep. NISTIR 7709, August 2010
2010
Earlier work this paper cites.
S. E. Baker, A. Hentz, K. W. Bowyer, and P. J. Flynn, “Degradation of iris recognition performance due to non-cosmetic prescription contact lenses,” Computer Vision and Image Understanding , vol. 114, no. 9, pp. 1030–1044, September 2010
2010
Earlier work this paper cites.
P. J. Phillips, F. Jiang, A. Narvekar, J. Ayyad, and A. J. O’Toole, “An other-race effect for face recognition algorithms,” Transactions on Applied Perception (TAP) , vol. 8, no. 2, pp. 14:1–14:11, January 2011
2011
Earlier work this paper cites.
ISO/IEC JTC1 SC37 Biometrics, ISO/IEC 19794-5:2005. Information technology – Biometric data interchange formats – Part 5: Face image data , International Organization for Standardization and International Electrotechnical Committee, June 2011
2011
Earlier work this paper cites.
K. O’Connor and S. J. Elliott, “The impact of gender on image quality, Henry classification and performance on a fingerprint recognition system,” in International Conference on Information Technology and Applications (ICITA) , 2011, pp. 304–307
2011
Earlier work this paper cites.
C. Gottschlich, T. Hotz, R. Lorenz, S. Bernhardt, M. Hantschel, and A. Munk, “Modeling the growth of fingerprints improves matching for adolescents,” Transactions on Information Forensics and Security (TIFS) , vol. 6, no. 3, pp. 1165–1169, September 2011
2011
Earlier work this paper cites.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, June 2011, pp. 1521–1528
2011
Earlier work this paper cites.
S. Lansing, “New York state COMPAS-probation risk and need assessment study: Examining the recidivism scale’s effectiveness and predictive accuracy,” New York State Division of Criminal Justice Services, Tech. Rep., September 2012
2012
Earlier work this paper cites.
A. J. O’Toole, P. J. Phillips, X. An, and J. Dunlop, “Demographic effects on estimates of automatic face recognition performance,” Image and Vision Computing , vol. 30, no. 3, pp. 169–176, March 2012
2012
Earlier work this paper cites.
B. F. Klare, M. J. Burge, J. C. Klontz, R. W. Vorder Bruegge, and A. K. Jain, “Face recognition performance: Role of demographic information,” Transactions on Information Forensics and Security (TIFS) , vol. 7, no. 6, pp. 1789–1801, October 2012
2012
Earlier work this paper cites.
S. Desmarais and J. Singh, Risk assessment instruments validated and implemented in correctional settings in the United States . Council of State Governments Justice Center, March 2013
2013
Earlier work this paper cites.
B. T. Ton and R. N. J. Veldhuis, “A high quality finger vascular pattern dataset collected using a custom designed capturing device,” in International Conference on Biometrics (ICB) . IEEE, June 2013, pp. 1–5
2013
Earlier work this paper cites.
G. H. Givens, J. R. Beveridge, P. J. Phillips, B. Draper, Y. M. Lui, and D. Bolme, “Introduction to face recognition and evaluation of algorithm performance,” Computational Statistics & Data Analysis , vol. 67, pp. 236–247, November 2013
2013
Earlier work this paper cites.
G. Schumacher, “Fingerprint recognition for children,” Joint Research Centre, Tech. Rep. EUR 26193 EN, September 2013
2013
Earlier work this paper cites.
M. Fairhurst, Age Factors in Biometric Processing . Institution of Engineering and Technology, 2013
2013
Earlier work this paper cites.
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in International Conference on Machine Learning (ICML) . JMLR, February 2013, pp. 325–333
2013
Earlier work this paper cites.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” in Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, June 2014, pp. 1701–1708
2014
Earlier work this paper cites.
S. Bharadwaj, M. Vatsa, and R. Singh, “Biometric quality: a review of fingerprint, iris, and face,” EURASIP journal on Image and Video Processing , vol. 2014, no. 1, p. 34, July 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
F. Pasquale, The black box society: The secret algorithms that control money and information . Harvard University Press, 2015
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, June 2015, pp. 815–823
2015
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman et al. , “Deep face recognition,” in British Machine Vision Conference (BMVC) . BMVA Press, September 2015, pp. 1–6
2015
Earlier work this paper cites.
S. Z. Li and A. K. Jain, Encyclopedia of biometrics . Springer, 2015
2015
Earlier work this paper cites.
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, “Certifying and removing disparate impact,” in International Conference on Knowledge Discovery and Data Mining . ACM, August 2015, pp. 259–268
2015
Earlier work this paper cites.
J. R. Beveridge, H. Zhang, B. A. Draper, P. J. Flynn et al. , “Report on the fg 2015 video person recognition evaluation,” in International Conference and Workshops on Automatic Face and Gesture Recognition (FG) , vol. 1. IEEE, May 2015, pp. 1–8
2015
Earlier work this paper cites.
K. Ricanek, S. Bhardwaj, and M. Sodomsky, “A review of face recognition against longitudinal child faces,” in International Conference of the Biometrics Special Interest Group (BIOSIG) . Gesellschaft für Informatik e.V., September 2015, pp. 15–26
2015
Earlier work this paper cites.
S. Yoon and A. K. Jain, “Longitudinal study of fingerprint recognition,” Proceedings of the National Academy of Sciences , vol. 112, no. 28, pp. 8555–8560, July 2015
2015
Earlier work this paper cites.
K. W. Bowyer and E. Ortiz, “Critical examination of the IREX VI results,” IET Biometrics , vol. 4, no. 4, pp. 192–199, December 2015
2015
Earlier work this paper cites.
P. Grother, J. R. Matey, and G. W. Quinn, “IREX VI: mixed-effects longitudinal models for iris ageing: response to bowyer and ortiz,” IET Biometrics , vol. 4, no. 4, pp. 200–205, December 2015
2015
Earlier work this paper cites.
ISO/IEC JTC1 SC37 Biometrics, ISO/IEC 29794-4:2017. Information technology – Biometric sample quality – Part 4: Finger image data , International Organization for Standardization and International Electrotechnical Committee, September 2015
2015
Earlier work this paper cites.
E. Dolgin, “The myopia boom,” Nature , vol. 519, no. 7543, pp. 276–278, March 2015
2015
Earlier work this paper cites.
Z. Tufekci, “Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency,” Journal on Telecommunications and High Technology Law , vol. 13, 2015
2015
Earlier work this paper cites.
C. Garvie, The perpetual line-up: Unregulated police face recognition in America . Georgetown Law, Center on Privacy & Technology, October 2016
2016
Earlier work this paper cites.
C. O’Neil, Weapons of math destruction: How big data increases inequality and threatens democracy . Broadway Books, 2016
2016
Earlier work this paper cites.
K. Bowyer and M. J. Burge, Handbook of iris recognition . Springer, 2016
2016
Earlier work this paper cites.
J. Daugman and C. Downing, “Searching for doppelgängers: assessing the universality of the IrisCode impostors distribution,” IET Biometrics , vol. 5, no. 2, pp. 65–75, June 2016
2016
Earlier work this paper cites.
A. Dantcheva, P. Elia, and A. Ross, “What else does your biometric data reveal? A survey on soft biometrics,” Transactions on Information Forensics and Security (TIFS) , vol. 11, no. 3, pp. 441–467, March 2016
2016
Earlier work this paper cites.
ISO/IEC JTC1 SC37 Biometrics, ISO/IEC 29794-1:2016. Information technology – Biometric sample quality – Part 1: Framework , International Organization for Standardization and International Electrotechnical Committee, September 2016
2016
Cited alongside, same era.
ISO/IEC JTC1 SC37 Biometrics, ISO/IEC 30107-1:2016. Information Technology – Biometric presentation attack detection – Part 1: Framework , International Organization for Standardization and International Electrotechnical Committee, January 2016
2016
Cited alongside, same era.
H. El Khiyari and H. Wechsler, “Face verification subject to varying (age, ethnicity, and gender) demographics using deep learning,” Journal of Biometrics and Biostatistics , vol. 7, no. 323, pp. 11–16, November 2016
2016
Cited alongside, same era.
M. Orcutt, “Are face recognition systems accurate? depends on your race.” https://www.technologyreview.com/s/601786/are-face-recognition-systems-accurate-depends-on-your-race/ , July 2016
2016
E. Awad, S. Dsouza, R. Kim, J. Schulz, J. Henrich, A. Shariff, J.-F. Bonnefon, and I. Rahwan, “The moral machine experiment,” Nature , vol. 563, no. 7729, pp. 59–64, November 2018
2018
Later among the works it cites.
European Union Agency for Fundamental Rights and Council of Europe, Handbook on European non-discrimination law . Publications Office of the European Union, February 2018
2018
Later among the works it cites.
K.-H. Yu and I. S. Kohane, “Framing the challenges of artificial intelligence in medicine,” BMJ Quality & Safety , vol. 28, no. 3, pp. 238–241, March 2019
2019
Later among the works it cites.
C. Castelluccia and D. Le Métayer, “Understanding algorithmic decision-making: Opportunities and challenges,” Institut national de recherche en informatique et en automatique, Tech. Rep. PE 624.261, March 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
G. Azzopardi, A. Greco, and M. Vento, “Gender recognition from face images with trainable COSFIRE filters,” in International conference on Advanced Video and Signal Based Surveillance (AVSS) . IEEE, August 2016, pp. 235–241
2016
Cited alongside, same era.
N. Diakopoulos, “Accountability in algorithmic decision making,” Communications of the ACM , vol. 59, no. 2, pp. 56–62, January 2016
2016
Cited alongside, same era.
J. A. Kroll, S. Barocas, E. W. Felten, J. R. Reidenberg, D. G. Robinson, and H. Yu, “Accountable algorithms,” University of Pennsylvania Law Review , vol. 165, 2016
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and C. Guestrin, “”why should I trust you?”: Explaining the predictions of any classifier,” in International Conference on Knowledge Discovery and Data Mining . ACM, 2016, pp. 1135–1144
2016
Cited alongside, same era.
M. Hardt, E. Price, N. Srebro et al. , “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems (NIPS) . Neural Information Processing Systems Foundation, December 2016, pp. 3315–3323
2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. Kirkpatrick, “Battling algorithmic bias: How do we ensure algorithms treat us fairly?” Communications of the ACM , vol. 59, no. 10, pp. 16–17, October 2016
2016
Cited alongside, same era.
E. J. Kindt, Privacy and Data Protection Issues of Biometric Applications . Springer, 2016
2016
Cited alongside, same era.
A. Ross, S. Banerjee, C. Chen, A. Chowdhury, V. Mirjalili, R. Sharma, T. Swearingen, and S. Yaday, “Some research problems in biometrics: The future beckons,” in International Conference on Biometrics (ICB) . IEEE, June 2019, pp. 1–8
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Liu, J. Chen, P. Fieguth, G. Zhao, R. Chellappa, and M. Pietikäinen, “From BoW to CNN: Two decades of texture representation for texture classification,” International Journal of Computer Vision , vol. 127, no. 1, pp. 74–109, January 2019
2019
Later among the works it cites.
J. J. Howard, Y. B. Sirotin, and A. R. Vemury, “The effect of broad and specific demographic homogeneity on the imposter distributions and false match rates in face recognition algorithm performance,” in International Conference on Biometric, Theory, Applications and Systems (BTAS) . IEEE, September 2019
2019
Later among the works it cites.
P. Grother, M. Ngan, and K. Hanaoka, “Ongoing face recognition vendor test (FRVT) part 3: Demographic effects,” National Institute of Standards and Technology, Tech. Rep. NISTIR 8280, December 2019
2019
Later among the works it cites.
S. Marcel, M. S. Nixon, J. Fierrez, and N.Evans, Handbook of Biometric Anti-spoofing: Presentation Attack Detection . Springer, 2019
2019
Later among the works it cites.
B. Lu, J. Chen, C. D. Castillo, and R. Chellappa, “An experimental evaluation of covariates effects on unconstrained face verification,” Transactions on Biometrics, Behavior, and Identity Science (TBIOM) , vol. 1, no. 1, pp. 42–55, January 2019
2019
Later among the works it cites.
I. D. Raji and J. Buolamwini, “Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products,” in Conference on AI Ethics and Society (AIES) . ACM, January 2019, pp. 429–435
2019
Later among the works it cites.
N. Srinivas, M. Hivner, K. Gay, H. Atwal, M. King, and K. Ricanek, “Exploring automatic face recognition on match performance and gender bias for children,” in Winter Applications of Computer Vision Workshops (WACVW) . IEEE, January 2019, pp. 107–115
2019
Later among the works it cites.
C. M. Cook, J. J. Howard, Y. B. Sirotin, J. L. Tipton, and A. R. Vemury, “Demographic effects in facial recognition and their dependence on image acquisition: An evaluation of eleven commercial systems,” Transactions on Biometrics, Behavior, and Identity Science (TBIOM) , vol. 1, no. 1, pp. 32–41, February 2019
2019
Later among the works it cites.
I. Hupont and C. Fernández, “DemogPairs: Quantifying the impact of demographic imbalance in deep face recognition,” in International Conference on Automatic Face & Gesture Recognition (FG) . IEEE, May 2019, pp. 1–7
2019
Later among the works it cites.
E. Denton, B. Hutchinson, M. Mitchell, and T. Gebru, “Detecting bias with generative counterfactual face attribute augmentation,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
R. V. Garcia, L. Wandzik, L. Grabner, and J. Krueger, “The harms of demographic bias in deep face recognition research,” in International Conference on Biometrics (ICB) . IAPR, June 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
K. S. Krishnapriya, K. Vangara, M. C. King, V. Albiero, and K. Bowyer, “Characterizing the variability in face recognition accuracy relative to race,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
V. Muthukumar, “Color-theoretic experiments to understand unequal gender classification accuracy from face images,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
N. Srinivas, K. Ricanek, D. Michalski, D. S. Bolme, and M. King, “Face recognition algorithm bias: Performance differences on images of children and adults,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
R. Vera-Rodriguez, M. Blazquez, A. Morales, E. Gonzalez-Sosa, J. C. Neves, and H. Proença, “FaceGenderID: Exploiting gender information in DCNNs face recognition systems,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
M. Wang, W. Deng, J. Hu, X. Tao, and Y. Huang, “Racial faces in-the-wild: Reducing racial bias by information maximization adaptation network,” in International Conference on Computer Vision (ICCV) . IEEE, November 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
——, “A study of age and ageing in fingerprint biometrics,” Transactions on Information Forensics and Security (TIFS) , vol. 14, no. 5, pp. 1351–1365, May 2019
2019
Later among the works it cites.
M. Brandão, “Age and gender bias in pedestrian detection algorithms,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
A. Amini, A. P. Soleimany, W. Schwarting, S. N. Bhatia, and D. Rus, “Uncovering and mitigating algorithmic bias through learned latent structure,” in Conference on AI, Ethics, and Society (AIES) . ACM, January 2019, pp. 289–295
2019
Later among the works it cites.
P. Terhörst, N. Damer, F. Kirchbuchner, and A. Kuijper, “Suppressing gender and age in face templates using incremental variable elimination,” in International Conference on Biometrics (ICB) . IAPR, June 2019, pp. 1–8
2019
Later among the works it cites.
——, “Unsupervised privacy-enhancement of face representations using similarity-sensitive noise transformations,” Applied Intelligence , vol. 49, no. 8, pp. 3043–3060, August 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Kortylewski, B. Egger, A. Schneider, T. Gerig, A. Morel-Forster, and T. Vetter, “Analyzing and reducing the damage of dataset bias to face recognition with synthetic data,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Wang, J. Zhao, M. Yatskar, K.-W. Chang, and V. Ordonez, “Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2019
2019
Later among the works it cites.
J. Galbally, P. Ferrara, R. Haraksim, A. Psyllos, and L. Beslay, “Study on face identification technology for its implementation in the Schengen information system,” Joint Research Centre, Tech. Rep. JRC-34751, July 2019
2019
Later among the works it cites.
C. Rathgeb, A. Dantcheva, and C. Busch, “Impact and detection of facial beautification in face recognition: An overview,” IEEE Access , vol. 7, pp. 152 667–152 678, October 2019
2019
Later among the works it cites.
Y. Koda, A. Takahashi, K. Ito, T. Aoki, S. Kaneko, and S. M. Nzou, “Development of 2,400ppi fingerprint sensor for capturing neonate fingerprint within 24 hours after birth,” in International Conference of the Biometrics Special Interest Group (BIOSIG) . IEEE, September 2019, pp. 1–7
2019
Later among the works it cites.
R. Haraksim, J. Galbally, and L. Beslay, “Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching,” Pattern Recognition , vol. 88, pp. 614–628, April 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
K. Holstein, J. W. Vaughan, H. Daumé, M. Dudik, and H. Wallach, “Improving fairness in machine learning systems: What do industry practitioners need?” in Conference on Human Factors in Computing Systems (CHI) . ACM, May 2019, pp. 1–16
2019
Later among the works it cites.
2019
Later among the works it cites.
B. Hutchinson and M. Mitchell, “50 years of test (un)fairness: Lessons for machine learning,” in Conference on Fairness, Accountability, and Transparency (FAT) . ACM, January 2019, pp. 49–58
2019
Later among the works it cites.
——, “Data quality and artificial intelligence – mitigating bias and error to protect fundamental rights,” European Union Agency for Fundamental Rights, Tech. Rep. TK-01-19-330-EN-N, June 2019
2019
Later among the works it cites.
P. C. Roy and V. N. Boddeti, “Mitigating information leakage in image representations: A maximum entropy approach,” in Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, June 2019, pp. 2586–2594
2019
Later among the works it cites.
E. Marasco, “Biases in fingerprint recognition systems: Where are we at?” in International Conference on Biometrics: Theory Applications and Systems (BTAS) . IEEE, September 2019, pp. 1–5
2019
Later among the works it cites.
N. Hallowell, L. Amoore, S. Caney, and P. Waggett, “Ethical issues arising from the police use of live facial recognition technology,” February 2019
2019
Later among the works it cites.
A. Uhl, S. Marcel, C. Busch, and R. N. J. Veldhuis, Handbook of Vascular Biometrics . Springer, 2020
2020
Closest in time.
2020
Closest in time.
V. Albiero, K. S. Krishnapriya, K. Vangara, K. Zhang, M. C. King, and K. W. Bowyer, “Analysis of gender inequality in face recognition accuracy,” in Winter Conference on Applications of Computer Vision (WACV) . IEEE, March 2020, pp. 81–89
2020
Closest in time.
K. S. Krishnapriya, V. Albiero, K. Vangara, M. C. King, and K. W. Bowyer, “Issues related to face recognition accuracy varying based on race and skin tone,” Transactions on Technology and Society (TTS) , vol. 1, no. 1, pp. 8–20, March 2020
2020
Closest in time.
2020
Closest in time.
J. Preciozzi, G. Garella, V. Camacho, F. Franzoni, L. Di Martino, G. Carbajal, and A. Fernandez, “Fingerprint biometrics from newborn to adult: A study from a national identity database system,” Transactions on Biometrics, Behavior, and Identity Science (TBIOM) , vol. 2, no. 1, pp. 68–79, January 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
M. Bruveris, J. Gietema, P. Mortazavian, and M. Mahadevan, “Reducing geographic performance differentials for face recognition,” in Winter Conference on Applications of Computer Vision (WACV) . IEEE, March 2020, pp. 98–106
2020
Closest in time.
P. Smith and K. Ricanek, “Mitigating algorithmic bias: Evolving an augmentation policy that is non-biasing,” in Winter Conference on Applications of Computer Vision (WACV) . IEEE, March 2020, pp. 90–97
2020
Closest in time.
2020
Closest in time.
P. Terhörst, M. L. Tran, N. Damer, F. Kirchbuchner, and A. Kuijper, “Comparison-level mitigation of ethnic bias in face recognition,” in International Workshop on Biometrics and Forensics (IWBF) . IEEE, April 2020, pp. 1–6
2020
Closest in time.
R. A. Calvo, D. Peters, and S. Cave, “Advancing impact assessment for intelligent systems,” Nature Machine Intelligence , vol. 2, no. 2, pp. 89–91, February 2020
2020
Closest in time.
D. Thakkar, “Global biometric market analysis: Trends and future prospects,” https://www.bayometric.com/global-biometric-market-analysis/ , August 2018, last accessed: August 8, 2026
2026
Closest in time.
Unique Identification Authority of India, “Aadhaar dashboard,” https://www.uidai.gov.in/aadhaar_dashboard/ , last accessed: August 8, 2026
2026
Closest in time.
A. Kortylewski, B. Egger, A. Schneider, T. Gerig, A. Morel-Forster, and T. Vetter, “Empirically analyzing the effect of dataset biases on deep face recognition systems,” in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, June 2018, pp. 2093–2102
2093
Closest in time.