The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds-Reference-Cited by-同舟云学术

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The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds

Published:2022 Issue: Volume:210 Page:212-217
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Atzori Andrea,Fenu Gianni,Marras Mirko

Publisher

Elsevier BV

Subject

General Engineering

Reference21 articles.

1. Facex-zoo: A pytorch toolbox for face recognition;Wang,2021

2. Rethinking common assumptions to mitigate racial bias in face recognition datasets;Gwilliam,2021

3. Analysis of gender inequality in face recognition accuracy;Albiero,2020

4. V. Albiero, K. W. Bowyer, Is face recognition sexist? no, gendered hairstyles and biology are, arXiv preprint arXiv:2008.06989 (2020).

5. A review of face recognition against longitudinal child faces;K.,2015

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. (Un)fair Exposure in Deep Face Rankings at a Distance;2023 IEEE International Joint Conference on Biometrics (IJCB);2023-09-25

2. Demographic Bias in Low-Resolution Deep Face Recognition in the Wild;IEEE Journal of Selected Topics in Signal Processing;2023-05

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