A new wrist vein biometric system

Das, Abhijit; Pal, Umapada; Ferrer, Miguel A.; Blumenstein, Michael

doi:10.1109/cibim.2014.7015445

Cited by 26 publications

(21 citation statements)

References 15 publications

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“…The reported EER is 1.23 %. However, based on the ROC curves provided in [11], the FNMR values for FMR≤0.1% are higher comparing to our results.…”

Section: Methodscontrasting

confidence: 78%

“…The paper [11], to the best of our knowledge, is the only one reporting recognition results using PUT Vein database. However, the precise evaluation protocol is unknown.…”

Section: Methodsmentioning

confidence: 99%

“…The task of the comparison stage is to compare two extracted vein patterns. Previous research on wrist vein recognition can be classified into cross-correlation versus machine learning based methods [11], [3], [1]. The most recent publication in the field [1] covers both of these comparison approaches.…”

Section: Preprocessingmentioning

confidence: 99%

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Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

Ņikišins

Eglitis

Anjos

et al. 2018

2018 International Workshop on Biometrics and Forensics (IWBF)

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Most of the research on vein biometrics addresses the problems of either palm or finger vein recognition with a considerably smaller emphasis on wrist vein modality. This paper paves the way to a better understanding of capabilities and challenges in the field of wrist vein verification. This is achieved by introducing and discussing a fully automatic crosscorrelation based wrist vein verification technique. Overcoming the limitations of ordinary cross-correlation, the proposed system is capable of compensating for scale, translation and rotation between vein patterns in a computationally efficient way. Introduced comparison algorithm requires only two cross-correlation operations to compensate for both translation and rotation, moreover the well known property of log-polar transformation of Fourier magnitudes is not involved in any form. To emphasize the veins, a two-layer Hessian-based vein enhancement approach with adaptive brightness normalization is introduced, improving the connectivity and the stability of extracted vein patterns. The experiments on the publicly available PUT Vein wrist database give promising results with FNMR of 3.75% for FMR ≈ 0.1%. In addition we make this research reproducible providing the source code and instructions to replicate all findings in this work.

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“…The reported EER is 1.23 %. However, based on the ROC curves provided in [11], the FNMR values for FMR≤0.1% are higher comparing to our results.…”

Section: Methodscontrasting

confidence: 78%

“…The paper [11], to the best of our knowledge, is the only one reporting recognition results using PUT Vein database. However, the precise evaluation protocol is unknown.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

Ņikišins

Eglitis

Anjos

et al. 2018

2018 International Workshop on Biometrics and Forensics (IWBF)

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“…With respect to texture-oriented feature representation, [49] employs a preprocessing consisting of adaptive histogram equalisation and enhancement using a discrete Meyer wavelet. Subsequently, LBP is extracted from patches with subsequent BoF representation in a spatial pyramid.…”

Section: Wrist Vein Recognition Toolchainmentioning

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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“…For the preprocessing approach, discretely implemented Meyer wavelet and histogram equalization with adaptive capability were proposed feature extraction using Pattern of Dense Local Binary known as D-LBP showed that it was able to achieve (EER) or the rate of an equal error on 0.79 [23]. Regarding this reason, it is intended to develop a wrist recognition system which is better than that of the D-LBP.…”

Section: A Related Workmentioning

confidence: 99%

Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

Fronitasari¹,

Basari²,

Gunawan³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Local Binary Pattern (LBP) is one of the well-known image recognition descriptors for texture-based images due to its superiority. LBP can represent texture well due to its ability to discriminate and compute efficiency. However, when it is used to describe textures that are barely visible, such as vein images (especially contactless vein), its discrimination ability is reduced, which leads to lower performance. LBP has extensively been implemented for features extraction in recognition system of hand, eye, face, eye, and other images. Nowadays, there are a lot of developments of hand recognition systems as a hand is a part of the body that can be easily used in the recognition process and it is easier to contact the sensor when taking the image (user-friendly). In particular, a hand consists of various parts that can be used, such as palm and fingers. Other parts like dorsal and wrist can also be used as they have unique characteristics, i.e., they are different from each other, and they do not change with ages. Changes in pixel intensity can be derived from skeletal vein images to distinguish individuals in palm vein recognition. In the previous paper, we proposed a method diagonal, vertical, horizontal local binary pattern (DVH-LBP) for implementing the palm vein recognition system successfully. Through this work, we improve our previous procedure and implement the improved method for recognizing wrist. In particular, this study proposes a new and robust directional extraction technique for encoding the functions of the wrist vein in a simple representation of binary numbers. Simulation results show the low equal error rate (ERR) of the proposed technique is 0.012, and the recognition rate is 99.4%.

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A new wrist vein biometric system

Cited by 26 publications

References 15 publications

Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

State of the Art in Vascular Biometrics

Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

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