State of the Art in Vascular Biometrics

Uhl, Andreas

doi:10.1007/978-3-030-27731-4_1

Cited by 33 publications

(27 citation statements)

References 285 publications

(363 reference statements)

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“…The overall best result was achieved for MC at 0°with an EER of 0.44% (±0. 15) where the number in brackets is the confidence interval. For all feature extraction methods, the worst results can be reported around 270°.…”

Section: Single Perspective Performance Resultsmentioning

confidence: 99%

“…In order to acquire a multi-perspective finger vein dataset, we designed a custom finger vein capture device tailored to this purpose. For more details on the general principle of a finger vein scanner and the vascular pattern recognition basics, the interested reader is referred to our open finger vein scanner chapter [6] and the introductory chapter [15] of this book, respectively. Our multi-perspective finger vein capture device is able to capture images from all around the finger's longitudinal axis (360 • ).…”

Section: Multi-perspective Finger Vein Capture Devicementioning

confidence: 99%

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Different Views on the Finger—Score-Level Fusion in Multi-Perspective Finger Vein Recognition

Prommegger

Kauba

Uhl

2019

Handbook of Vascular Biometrics

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In finger vein recognition, the palmar view of the finger is used almost exclusively, with some exceptions where the dorsal view is utilised. Only little attention has been paid to all other views around the finger's longitudinal axis. We established a multi-perspective finger vein dataset comprising of views all around the finger's longitudinal axis, captured using our self-developed rotating multi-perspective finger vein capture device. The performance of the single views is evaluated using common finger vein recognition algorithms. Based on these single view scores, several score-level fusion experiments involving different fusion strategies are carried out in order to determine the best performing set of views and feature extraction methods to be fused in terms of recognition accuracy while minimising the number of views involved. Our experimental results show that the recognition performance can be significantly improved over the best performing single view one with as few as two views and two-feature extraction methods involved.

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Section: Single Perspective Performance Resultsmentioning

confidence: 99%

Section: Multi-perspective Finger Vein Capture Devicementioning

confidence: 99%

Different Views on the Finger—Score-Level Fusion in Multi-Perspective Finger Vein Recognition

Prommegger

Kauba

Uhl

2019

Handbook of Vascular Biometrics

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“…To acquire the network structure of blood vessels underneath the human skin, a vascularbased recognition system uses near-infrared light to reflect or transmit images of blood vessels, since they are almost invisible in normal lighting conditions [10]. The most commonly used vascular biometric solutions use hand-oriented modalities, such as finger vein, palm vein, hand dorsal vein, and wrist vein recognition, as well as eye-oriented modalities, such as retina and sclera recognition [11].…”

Section: Physiological/biological (Intrinsic) Human Characteristicsmentioning

confidence: 99%

Biometric-Based Human Recognition Systems: An Overview

Palma¹,

Montessoro²

2022

Recent Advances in Biometrics

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With the proliferation of automated systems for reliable and highly secure human authentication and identification, the importance of technological solutions in biometrics is growing along with security awareness. Indeed, conventional authentication methodologies, consisting of knowledge-based systems that make use of something you know (e.g., username and password) and token-based systems that make use of something you have (e.g., identification card), are not able to meet the strict requirements of reliable security applications. Conversely, biometric systems make use of behavioral (extrinsic) and/or physiological (intrinsic) human characteristics, overcoming the security issues affecting the conventional methods for personal authentication. This book chapter provides an overview of the most commonly used biometric traits along with their properties, the various biometric system operating modalities as well as various security aspects related to these systems. In particular, it will be discussed the different stages involved in a biometric recognition process and further discuss various threats that can be exploited to compromise the security of a biometric system. Finally, in order to evaluate the systems’ performance, metrics must be adopted. The most widely used metrics are, therefore, discussed in relation to the provided system accuracy and security, and applicability in real-world deployments.

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“…Methodological research on FVS has focussed both on instrumental issues-including the selection of the appropriate wavelength of NIR [9] and the design of new image acquisition devices [10,11]-and on novel algorithmic solutions at the preand post-processing stage [12]. Various types of algorithms have been developed to improve the efficiency of biometric systems [13,14], the overwhelming majority of which involve the use of neural networks, deep learning methods, or other advanced image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Recognition of the finger vascular system using multi‐wavelength imaging

et al. 2022

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There has recently been intensive development of methods for identification and personal verification using the human finger vascular system (FVS). The primary focus of these efforts has been the increasingly sophisticated methods of image processing, and frequently employing machine learning. In this article, we present a new concept of imaging in which the finger vasculature is illuminated using different wavelengths of light, generating multiple FVS images. We hypothesised that the analysis of these image sets, instead of individual images, could increase the effectiveness of identification. Analyses of data from over 100 volunteers, using five different deterministic methods for feature extraction, consistently demonstrated improved identification efficiency with the addition of data obtained from another wavelength. The best results were seen for combinations of diodes between 800 and 900 nm. Finger vascular system observations outside this range were of marginal utility. The knowledge gained from this experiment can be utilised by designers of biometric recognition devices leveraging FVS technology. Our results confirm that developments in this field are not restricted to image processing algorithms, and that hardware innovations remain relevant.

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State of the Art in Vascular Biometrics

Cited by 33 publications

References 285 publications

Different Views on the Finger—Score-Level Fusion in Multi-Perspective Finger Vein Recognition

Different Views on the Finger—Score-Level Fusion in Multi-Perspective Finger Vein Recognition

Biometric-Based Human Recognition Systems: An Overview

Recognition of the finger vascular system using multi‐wavelength imaging

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