An Available Open-Source Vein Recognition Framework

Kauba, Christof; Uhl, Andreas

doi:10.1007/978-3-030-27731-4_4

Cited by 14 publications

(10 citation statements)

References 42 publications

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“…All of them can be applied to finger as well as hand vein images, respectively. Further details regarding these methods are given in [34]. Examples of binary output images are shown in Fig.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Inverse Biometrics: Generating Vascular Images From Binary Templates

Kauba

Kirchgasser

Mirjalili

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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In this work, we investigate the possibility of generating grayscale images of finger and hand vein patterns from their corresponding binary templates. This exercise would allow us to determine the invertibility of vascular templates, which has implications in biometric security and privacy. The transformation from binary features to a gray-scale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger-vein datasets and 3 hand-vein datasets. Results indicate that (a) it is possible to reconstruct the considered vascular images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization). The results of this research further underscore the need for properly securing biometric templates, even if they are of binary nature.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Inverse Biometrics: Generating Vascular Images From Binary Templates

Kauba

Kirchgasser

Mirjalili

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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“…It employs the SqueezeNet architecture [38] using the triplet loss function together with hard triplet online selection as described in [39]. All other experiments have been executed using the publicly available PLUS OpenVein Finger‐ and Hand‐Vein Toolkit [40]. Download links for the PLUS OpenVein Finger‐ and Hand‐Vein Toolkit , the DFVR implementation as well as the trained CNN model are provided on http://www.wavelab.at/sources/Prommegger20b.…”

Section: Methodsmentioning

confidence: 99%

A fully rotation invariant multi‐camera finger vein recognition system

Prommegger

Uhl

2021

IET Biometrics

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Finger vein recognition systems utilize the venous pattern within the fingers to recognize subjects. It has been shown that the alignment of the acquired samples has a major impact on the recognition accuracy of such systems. Although a lot of work has been done in this field, there is still no approach that solves all kind of finger misplacements. In particular, longitudinal finger rotation still causes major problems. As the capturing devices evolve towards contactless acquisition, solutions to alignment problems become more important. As an alternative to rotation detection and correction, the problem can also be addressed by acquiring the vein pattern from different perspectives. This article presents a novel multi-camera finger vein recognition system that captures the vein pattern from multiple perspectives during enrolment and recognition. Contrary to existing multicamera solutions that use the same capturing device for enrolment and recognition, the capturing devices for the proposed system differ in the configuration of the acquired perspectives. The cameras of the devices are positioned so that the recognition rates around the finger are high and that the number of cameras needed is kept to a minimum. The experimental results confirm the rotation invariance of the proposed approach. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Finally, the "PLUS OpenVein Finger-and Hand-Vein SDK 12 " is currently the largest open-source toolbox for vascular-related biometric recognition and is a feature extraction and template comparison/evaluation framework for finger and hand vein recognition implemented in MATLAB. A chapter in this book [116] is dedicated to a detailed description of this software.…”

Section: Eye-based Vascular Traitsmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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An Available Open-Source Vein Recognition Framework

Cited by 14 publications

References 42 publications

Inverse Biometrics: Generating Vascular Images From Binary Templates

Inverse Biometrics: Generating Vascular Images From Binary Templates

A fully rotation invariant multi‐camera finger vein recognition system

State of the Art in Vascular Biometrics

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