Predicting Gender From Iris Texture May Be Harder Than It Seems

Kuehlkamp, Andrey; Bowyer, Kevin W.

doi:10.1109/wacv.2019.00101

Cited by 13 publications

(22 citation statements)

References 14 publications

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“…The same observation is noted for other CNN models used in the study. In [14], Kuehlkamp et al suggest that discriminative power of the iris texture for gender is weak and that the gender-related information is primarily in the periocular region which is supported by our observations as well.…”

Section: Cnnsupporting

confidence: 90%

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Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

Krishnan¹,

Almadan²,

Rattani³

2021

Preprint

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A number of studies suggest bias of the face biometrics, i.e., face recognition and soft-biometric estimation methods, across gender, race, and age-groups. There is a recent urge to investigate the bias of different biometric modalities toward the deployment of fair and trustworthy biometric solutions. Ocular biometrics has obtained increased attention from academia and industry due to its high accuracy, security, privacy, and ease of use in mobile devices. A recent study in 2020 also suggested the fairness of ocular-based user recognition across males and females. This paper aims to evaluate the fairness of ocular biometrics in the visible spectrum among age-groups; young, middle, and older adults. Thanks to the availability of the latest large-scale 2020 UFPR ocular biometric dataset, with subjects acquired in the age range 18 -79 years, to facilitate this study. Experimental results suggest the overall equivalent performance of ocular biometrics across gender and agegroups in user verification and gender-classification. Performance difference for older adults at lower false match rate and young adults was noted at user verification and age-classification, respectively. This could be attributed to inherent characteristics of the biometric data from these age-groups impacting specific applications, which suggest a need for advancement in sensor technology and software solutions.

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Section: Cnnsupporting

confidence: 90%

“…A possible explanation for this observation could be the stable and distinct gender cues for middle aged adults when compared to young and older adults. Further, the periocular region contains the gender cues (also confirmed in [14]) which differ across males and females.…”

Section: Key Findingsmentioning

confidence: 91%

Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

Krishnan¹,

Almadan²,

Rattani³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We also used person-disjoint to prevent bias, meaning no subjects were shared between the training and validation data sets. 22 As our data set is not large, we used fivefold cross-validation to train and evaluate the performance of the DL model. 23 In cross-validation, the entire data set was split into five groups, and four groups were used as training data, whereas one group was used for validation.…”

Section: Methodsmentioning

confidence: 99%

Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning

Zheng

et al. 2021

Trans. Vis. Sci. Tech.

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“…The interest in privacy-enhancing mechanisms is not limited only to facial images. With the development of automatic recognition techniques and improvements in their capabilities, it is today possible to extract potentially sensitive information from a wide variety of biometric modalities, e.g., [311], [312], [313], [314]. B-PETs are, therefore, becoming increasingly relevant across the board and research efforts, focusing on privacy protection with modalities other than faces are expected to intensify in the future.…”

Section: Visual Privacy Beyond Facesmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

104

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Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

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Predicting Gender From Iris Texture May Be Harder Than It Seems

Cited by 13 publications

References 14 publications

Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

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