Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Abstract

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioural characteristics of human beings, which enable for individuals to be reliably recognised using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labelled as "racist" or "biased" by the media, non-governmental organisations, and researchers alike. The main contributions of this article are: (1) an overview of the topic of algorithmic bias in the context of biometrics, (2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation, (3) a discussion of the pertinent technical and social matters, and (4) an outline of the remaining challenges and future work items, both from technological and social points of view.

15 pages, 3 figures, 3 tables. Submitted to IEEE Transactions on Technology and Society. Update after first round of peer review

References in corpus (6)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Improving fairness in machine learning systems: What do industry practitioners need?
• 50 Years of Test (Un)fairness: Lessons for Machine Learning
• An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification
• Modeling the growth of fingerprints improves matching for adolescents
• Age and gender bias in pedestrian detection algorithms

Cited by in corpus (7)

• Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment
• Evaluating Proposed Fairness Models for Face Recognition Algorithms
• Review of Demographic Fairness in Face Recognition
• Impact of Facial Tattoos and Paintings on Face Recognition Systems
• Demographic Variability in Face Image Quality Measures
• DemoBias: An Empirical Study to Trace Demographic Biases in Vision Foundation Models