Author:
Schuckers Michael,Purnapatra Sandip,Fatima Kaniz,Hou Daqing,Schuckers Stephanie
Publisher
Springer Nature Switzerland
Reference19 articles.
1. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of Machine Learning Research, Conference on Fairness, Accountability, and Transparency, pp. 1–15 (2018)
2. Buolamwini, J.A.: Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers, M.Sc. Thesis (2017). http://hdl.handle.net/1721.1/114068. Accessed 10 Jul 2022
3. Grother, P., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT) part 3: demographic effects. United States National Institute of Standards and Technology, Technical report, NIST.IR 8280 (2019). https://doi.org/10.6028/NIST.IR.8280
4. Cook, C.M., Howard, J.J., Sirotin, Y.B., Tipton, J.L., Vemury, A.R.: Demographic effects in facial recognition and their dependence on image acquisition: an evaluation of eleven commercial systems. IEEE Trans. Biometrics Behav. Identity Sci. 1(1), 32–41 (2019)
5. de Freitas Pereira, T., Marcel, S.: Fairness in biometrics: a figure of merit to assess biometric verification systems. IEEE Trans. Biometrics Behav. Identity Sci. 4(1), 19–29 (2022)
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