Some Research Problems in Biometrics: The Future Beckons

Ross, Arun; Banerjee, Sudipta; Chen, Cunjian; Chowdhury, Anurag; Mirjalili, Vahid; Sharma, Rajendra; Swearingen, Thomas; Yadav, Shivangi

doi:10.1109/icb45273.2019.8987307

Cited by 55 publications

(27 citation statements)

References 30 publications

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“…''To overcome some of the constraints currently affecting biometric systems, the design of novel sensors is required''. This quote has been extracted from a 2019 paper by Ross et al, reflecting upon some of the fundamental issues that still need to be addressed in biometrics [47]. The statement reinforces the idea already expressed by Jain et al in their 2016 paper that reviewed 50 years of automated biometric systems [46].…”

Section: Stage 1: Touchless Full-3d Fingerprint Acquisition Based On Laser Triangulationsupporting

confidence: 56%

3D-FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing

2020

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The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself. There are already a small number of previous studies that have already started scratching the surface of 3D fingerprint recognition and that should not go overlooked. However, the vast majority of these few successful approaches published so far, are based on the reconstruction of fingerprints from multiple 2D images acquired with different lighting conditions (photometric stereo 3D reconstruction) or acquired from different angles (stereo vision 3D reconstruction). Such reconstruction methods lead in general to 2D fingerprints wrapped over the overall volume of the finger. These volumetric fingerprints have shown some promising performance, but still miss the real depth information of the ridge pattern, which, in the best case scenario, is coarsely estimated during the error-prone reconstruction process. In the present work we take one step further, directly acquiring for the first time in a consistent and repeatable manner, full-3D fingerprint models stored as point-clouds, where each point is defined by its [x, y, z] coordinates. This way, the 3D data is directly measured by the sensor, with no post-processing reconstruction stage required. The complete recognition system developed represents as well an alternative to traditional technology based on minutiae detection. It shows that image-based processing algorithms and descriptors can be successfully applied to the new full-3D data, reaching very competitive results and confirming the high distinctiveness of the models.INDEX TERMS 3D data processing, fingerprint recognition, laser sensing.

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Section: Stage 1: Touchless Full-3d Fingerprint Acquisition Based On Laser Triangulationsupporting

confidence: 56%

3D-FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing

2020

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“…These properties combine to make the iris very attractive as a biometric identifier. As is the case with every biometric mode, there is ongoing research related to various challenges, including such things as presentation attack detection, dealing with eye diseases, application to identification of newborns, understanding aging processes and developing algorithms that are agnostic to age-related changes, and assessing reliability of post-mortem iris recognition [3].…”

Section: Introductionmentioning

confidence: 99%

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

et al. 2020

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Post-mortem biometrics entails utilizing the biometric data of a deceased individual for determining or verifying human identity. Due to fundamental biological changes that occur in a person's biometric traits after death, post-mortem data can be significantly different from ante-mortem data, introducing new challenges for biometric sensors, feature extractors and matchers. This paper surveys research to date on the problem of using iris images acquired after death for automated human recognition. A comprehensive review of existing literature is complemented by a summary of the most recent results and observations offered in these publications. This survey is unique in several elements. Firstly, it is the first publication to consider iris recognition where gallery images are acquired before death (perimortem images) and the probe images are acquired after death from the same subjects. Secondly, results are presented from the largest database of peri-mortem and post-mortem iris images, collected from 213 subjects by two independent institutions located in the U.S. and Poland. Thirdly, post-mortem recognition viability is assessed using more than 20 iris recognition algorithms, ranging from the classic (e.g., Gabor filteringbased) to the modern (e.g., deep learning-based). Finally, we provide a medically informed commentary on post-mortem iris, analyze the reasons for recognition failures, and identify key directions for future research.

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“…The issue of fairness in data-driven computer vision systems has gained prominence, especially due to the presence of potential biases in the training data [304]. Since biometric systems rely heavily on training data, it is imperative that evaluation methodologies explicitly address the issue of fairness and bias [305], [306], [307], [308].…”

Section: Fairness and Biasmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

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et al. 2021

IEEE Trans.Inform.Forensic Secur.

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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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Some Research Problems in Biometrics: The Future Beckons

Cited by 55 publications

References 30 publications

3D-FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing

3D-FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

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