Demographic Bias: A Challenge for Fingervein Recognition Systems?

Drozdowski, Pawel; Prommegger, Bernhard; Wimmer, Georg; Schraml, Rudolf; Rathgeb, Christian; Uhl, Andreas; Busch, Christoph

doi:10.23919/eusipco47968.2020.9287722

Cited by 11 publications

(18 citation statements)

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“…A considerable amount of work has been done to investigate (demographic) bias and fairness in face recognition systems, e.g., [17]- [19], and potentially sensitive face-related tasks, such as age estimation [20], face image quality assessment [21], privacy protection [22], and face-morph detection [23] to name a few examples. Similar studies were also presented for fingerprints [24], [25], finger vein [26], and palm print [27] recognition systems among others. While much of this work aimed at identifying the presence of bias in various (learningbased) biometric systems and algorithms (e.g., [17], [20], [24], [26]), a small number of works also tried to investigate causes of the observed performance differentials for different data groups, e.g., [19], [28].…”

Section: Background and Related Work A Bias And Fairness In Biometricssupporting

confidence: 65%

“…Similar studies were also presented for fingerprints [24], [25], finger vein [26], and palm print [27] recognition systems among others. While much of this work aimed at identifying the presence of bias in various (learningbased) biometric systems and algorithms (e.g., [17], [20], [24], [26]), a small number of works also tried to investigate causes of the observed performance differentials for different data groups, e.g., [19], [28]. The insight and observations made by these studies provided critical understanding of the bias-related behavior of existing biometric algorithms and contributed towards various bias mitigation measures, e.g., [29]- [31].…”

Section: Background and Related Work A Bias And Fairness In Biometricssupporting

confidence: 65%

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Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Vitek

Das

Lucio

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclerabased biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

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Section: Background and Related Work A Bias And Fairness In Biometricssupporting

confidence: 65%

Section: Background and Related Work A Bias And Fairness In Biometricssupporting

confidence: 65%

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Vitek

Das

Lucio

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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“…In recent years, the research field in biometrics have also evaluated the bias and fairness of these systems as well as developing novel methods for bias mitigation. Most of the studies that we have analysed do not take into account the most relevant state-of-the-art definitions of fairness in machine learning, and some of them argued that their experimental evaluation suggests lack of bias in score distributions Drozdowski et al (2021). Moreover, recent studies of fairness in machine learning have proved to be generally impossible to satisfy several fairness criteria simultaneously Garg et al (2020); Chouldechova (2017); Zhao and Gordon (2019); Kleinberg (2018).…”

Section: A Theoretical Approach Towards Fairness In Biometricsmentioning

confidence: 99%

“…finger, palm or human eye veins Uhl et al (2020). These four systems are proposed in Drozdowski et al (2021) to assess demographic bias by differences on score distribution statistics (mean and standard deviation) of genuine and impostor attempts are evaluated. The conclusion achieve is that statistically significant biases in score distributions do not exist and the authors proposed to evaluate this framework in the future with more individuals, given that the number of subjects in each of the databases is very small.…”

Section: The Impossibility Of Unbiased Biometric Systemsmentioning

confidence: 99%

“…By examining the recent literature on fairness in biometrics, we observe a lack of engagement with the state-of-the-art of fairness. Notably, a recent work in finger vein recognition systems have suggested a lack of bias for the tested biometric algorithms Drozdowski et al (2021). Although finger vein systems have not been implemented for migration control, we propose to empirically demonstrate the impossibility of fairness examining the systems used in this work.…”

Section: Introductionmentioning

confidence: 97%

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There is an elephant in the room: Towards a critique on the use of fairness in biometrics

Valdivia¹,

Corbera-Serrajòrdia²,

Swianiewicz³

2021

Preprint

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In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal dismissed an asylum appeal basing the decision on the output of a biometric system, alongside other discrepancies. The fingerprints of the asylum seeker were found in the EU's asylum fingerprint database and registered in 2016, which contradicted the appellant's account that he left Iraq in 2017. The Tribunal found this biometric evidence 'unequivocal' and denied the asylum claim. Nowadays, the proliferation of biometric systems in our societies is shaping public debates around its political, social and ethical implications. Yet whilst concerns towards the racialised use of this technology for migration control or law enforcement have been on the rise, investment in the biometrics industry and innovation is increasing considerably. Moreover, fairness has also been recently adopted by biometrics to analyse demographic bias of biometric systems. Thousands of studies have been recently published to mitigate bias and discrimination on facial recognition, fingerprints or finger veins, among others, even suggesting a lack of bias on these systems. However, algorithmic fairness cannot distribute justice in scenarios which are broken or intended purpose is to discriminate, such as biometrics deployed at the border. In this paper, we offer a critical reading of recent debates about biometric fairness and show its limitations drawing on research in fairness in machine learning and critical border studies. Building on previous fairness demonstrations, we prove that biometric fairness criteria are mathematically mutually exclusive. Then, the paper moves on illustrating empirically that a fair biometric system is not possible by reproducing experiments from previous works. Finally, we discuss the politics of fairness in biometrics systems by situating the debate around biometrics used at the border. We claim that bias and error rates have different impact on citizens and asylum seekers. Fairness has overshadowed the elephant in the room of biometrics, focusing on the demographic biases and ethical discourses of these algorithms rather than examine how these systems reproduce historical and political injustices in these contexts.

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