Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

Fang, Meiling; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

doi:10.23919/eusipco47968.2020.9287321

Cited by 32 publications

(30 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The exact set of attributes that can be included under the biometric bias term are still an open discussion issue as concluded in the EAB demographic fairness in biometric systems workshop [55]. The phenomena of bias in face biometrics were found in several disciplines such as presentation attack detection [19], [33], the estimation of facial characteristics [70], [15], and the assessment of face image quality [71]. In some previous works, factors that affect the recognition performance were also known as covariates [45], [44].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Terhörst

Kolf

Huber

et al. 2022

IEEE Trans. Technol. Soc.

Self Cite

View full text Add to dashboard Cite

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyse FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Terhörst

Kolf

Huber

et al. 2022

IEEE Trans. Technol. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To our knowledge there is only one research conducted to study the fairness of presentation attack detection technology [8]. This paper is related to iris-based presentation attack detection.…”

Section: Bias In Presentation Attack Detection Systemsmentioning

confidence: 99%

“…In [8], three iris PAD algorithms were explored in order to observe their behavior toward different demographic groups. The NDCLD database proposed in [23] was used in this study but due to its limited diversity, only gender bias was measured.…”

Section: Bias In Presentation Attack Detection Systemsmentioning

confidence: 99%

“…Using different dataset scenarios for training and testing, both the performance and results of iris PAD systems were monitored, analyzed, and compared for men and women. Researchers in [8] concluded that presentation attacks targeting women were noticeably harder to detect.…”

Section: Bias In Presentation Attack Detection Systemsmentioning

confidence: 99%

“…One research was conducted to discover whether iris-based presentation attack detection models are biased or not. Its results showed unfairness in detecting attacks for subjects from different genders [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Study of Gender Bias in Face Presentation Attack and Its Mitigation

Alshareef¹,

Yuan²,

Atay³

2021

Future Internet

View full text Add to dashboard Cite

In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.

show abstract

Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing

Abduh,

Ivrissimtzis

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

Cited by 32 publications

References 22 publications

A Comprehensive Study on Face Recognition Biases Beyond Demographics

A Comprehensive Study on Face Recognition Biases Beyond Demographics

A Study of Gender Bias in Face Presentation Attack and Its Mitigation

Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing

Contact Info

Product

Resources

About