Algorithms for a novel touchless bimodal palm biometric system

Ņikišins, Oļegs; Eglitis, Teodors; Pudžs, Mihails; Greitāns, Modris

doi:10.1109/icb.2015.7139107

Cited by 11 publications

(19 citation statements)

References 12 publications

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“…Also, [133] applies SIFT to binarised patterns after enhancement, while [193] employs SIFT, SURF and Affine-SIFT as feature extraction to histogram equalised sample data. An approach related to histogram of gradients (HOG) is applied in [72,187], where after the application of matched filters localised histograms encoding vessel directions (denoted as "histogram of vectors") are generated as features. It is important to note that this work is based on a custom sensor device which is able to apply reflected light as well as transillumination imaging [72].…”

Section: Palm Vein Recognition Toolchainmentioning

confidence: 99%

“…The recon-structed images are evaluated in terms of quality but unfortunately no recognition experimentation is conducted. A feature-level fusion of their techniques applied to palm vein and palmprint data is proposed in [187,263,266]. The mentioned ResNet approach [309] is also applied to both modalities with subsequent feature fusion.…”

Section: Palm Vein Recognition Toolchainmentioning

confidence: 99%

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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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Section: Palm Vein Recognition Toolchainmentioning

confidence: 99%

Section: Palm Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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“…The main task of the preprocessor is to find the ROI in the input image. In palm and finger vein recognition systems, characteristic points of the palm contour, such as finger gaps and tips, are usually involved in the ROI localization [3], [4], [5]. Authors in [3] applied the same strategy using wrist joints as referencing points for ROI detection in the wrist images.…”

Section: Preprocessingmentioning

confidence: 99%

“…An example of ROI and correspondingly preprocessed input image are displayed in Figure 2. The ROI is of an arbitrary shape, similar to the idea in [4], maximizing the area to extract the vein pattern from.…”

Section: A 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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Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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Algorithms for a novel touchless bimodal palm biometric system

Cited by 11 publications

References 12 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

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

Review of palm vein recognition

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