Multi-Perspective Finger-Vein Biometrics

Handbook of Vascular Biometrics

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.

Section: Multi-perspective Finger Vein Biometricsmentioning

confidence: 99%

Section: Multi-perspective Finger Vein Biometricsmentioning

confidence: 99%

Section: Single Modality (Finger Vein Only) Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Prommegger

Kauba

Handbook of Vascular Biometrics

2019

Self Cite

“…A topic of current intensive research is template comparison techniques (and suited feature representations) enabling the compensation of finger rotation and finger deformation [76,94,163,203,204,299]. Somewhat related is the consideration multi-perspective finger vein recognition, where two [153] and multiple [205] perspectives are fused to improve recognition results of single-perspective schemes. A chapter in this handbook contains the proposal of a dedicated three-view finger vein scanner [258], while an in-depth analysis of multi-perspective fusion techniques is provided in another one [206].…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Handbook of Vascular Biometrics

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.

An Available Open-Source Vein Recognition Framework

Kauba

Advances in Computer Vision and Pattern Recognition

2019

Self Cite

An available recognition toolkit is one of the basic requirements for conducting research in finger-and hand vein recognition. Currently, there is only one comprehensive, open-source, software package available, which includes a full finger vein recognition toolkit. We present a novel, full fledged vein recognition software framework implemented in MATLAB. Our PLUS OpenVein software package contains various well-established and state-of-the-art vein enhancement, feature extraction and comparison schemes. Moreover, it contains tools to evaluate the recognition performance and provides functions to perform feature-and score-level fusion. It is fully open source and available free of charge for research and non-commercial purposes. This vein recognition framework has already been used in several papers and can be a valuable tool for other researchers working in vein recognition.