OpenVein—An Open-Source Modular Multipurpose Finger Vein Scanner Design

Advances in Computer Vision and Pattern Recognition

2019

Self Cite

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.

“…1.4. The two chapters in this handbook describing sensor technologies provide further details on this topic [113,258]. 2.…”

Section: Algorithms In the Recognition Toolchainmentioning

confidence: 99%

“…A minority of papers discusses certain stages in isolated manner, as also evaluation is more difficult in this setting (e.g. manuscripts on sensor construction, as also contained in this handbook [113,258], sample quality (see Sect. 1.6), or RoI determination (e.g.…”

Section: Algorithms In the Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Advances in Computer Vision and Pattern Recognition

2019

Self Cite

“…In [5], we established a larger dorsal finger vein dataset captured using both of our proposed open-source finger vein capture devices, which design is decribed in Chap. 3 of this book [6].…”

Section: Multi-perspective Finger Vein Biometricsmentioning

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%

“…Instead of typical NIR LEDs the illumination module consists of five NIR laser modules with a peak emission wavelength of 808 nm placed in a strip. Laser diodes have several advantages over LEDs, especially, if the finger is not placed directly on top of the illumination module as mentioned in Chapter [6]. Due to the rotating principle of the biometric capture device, it is not possible for the finger to touch the illumination module, which prevents the use of LEDs without impacting the image quality.…”

Section: Multi-perspective Finger Vein Capture Devicementioning

confidence: 99%

See 1 more Smart Citation

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

Prommegger

Kauba

Advances in Computer Vision and Pattern Recognition

2019

Self Cite

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.

Cancellable Biometrics for Finger Vein Recognition—Application in the Feature Domain

Kirchgasser

Kauba

Handbook of Vascular Biometrics

2019

Self Cite

Privacy preservation is a key issue that has to be addressed in biometric recognition systems. Template protection schemes are a suitable way to tackle this task. Various template protection approaches originally proposed for other biometric modalities have been adopted to the domain of vascular pattern recognition. Cancellable biometrics are one class of these schemes. In this chapter, several cancellable biometrics methods like block re-mapping and block warping are applied in the feature domain. The results are compared to previous results obtained by the use of the same methods in the image domain regarding recognition performance, unlinkability and the level of privacy protection. The experiments are conducted using several well-established finger vein recognition systems on two publicly available datasets. Furthermore, an analysis regarding subject-versus system-dependent keys in terms of security and recognition performance is done.