Contactless finger-vein verification based on oriented elements feature

Ma, Hui; Zhang, S.Y.

doi:10.1016/j.infrared.2018.12.021

Cited by 18 publications

(6 citation statements)

References 22 publications

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“…This work showed that the degradation in recognition performance resulting from touchless acquisition can be addressed using finger misplacement corrections. On the other hand, the approach presented in [117] extracts a region of interest from captured samples and uses an oriented element feature extraction scheme to improve robustness.…”

Section: A Touchless Hand-based Biometricsmentioning

confidence: 99%

Biometrics in the Era of COVID-19: Challenges and Opportunities

Gomez‐Barrero

Drozdowski

Rathgeb

et al. 2022

IEEE Trans. Technol. Soc.

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Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voiceand hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touchless fingerprint-and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.

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Section: A Touchless Hand-based Biometricsmentioning

confidence: 99%

Biometrics in the Era of COVID-19: Challenges and Opportunities

Gomez‐Barrero

Drozdowski

Rathgeb

et al. 2022

IEEE Trans. Technol. Soc.

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“…Images of the finger or hand are captured with NIR illumination, since light at NIR frequencies is absorbed differently by hemoglobin and the skin, thereby allowing for the detection of vein patterns. Touchless fingervein and palmvein sensors have been developed [103,104,105], though the lack of any control in the collection process typically significant rotation and translation variation. The quality of the capturing device as well as strategies to compensate for nuisance variation are hence key to the collection of high quality images and reliable performance.…”

Section: Touchless Hand-based Biometricsmentioning

confidence: 99%

“…This work showed that the degradation in recognition performance resulting from touchless acquisition can be addressed using finger misplacement corrections. On the other hand, the approach presented in [104] extracts a region of interest from captured samples and uses an oriented element feature extraction scheme to improve robustness.…”

Section: Touchless Hand-based Biometricsmentioning

confidence: 99%

Biometrics in the Era of COVID-19: Challenges and Opportunities

Gomez‐Barrero¹,

Drozdowski²,

Rathgeb³

et al. 2021

Preprint

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Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voice-and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint-and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.

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“…With the widely application of Internet technology and the rapidly increasing of online fraud cases, verifying an identification to protect personal information and property have become a challenging task for information security technology.Biometric recognition technology used human physiological or behavioral characteristics for personal identity authentication has been widely investigated in past years. Currently, the physiological characteristics used for verification can be divided into two categories [1] : extrinsic modalities, such as fingerprints [2] ,iris, and faces [3] , and intrinsic modalities, such as palm veins and finger veins [4] .On the contrary, the extrinsic modalities are concealed in our body and do not leave on touched objects. In addition, extrinsic modalities are difficult to observe under visible light and effectively collected by specific sensors.At the same time,the vein patterns can only be collected from living bodies, resulting in liveliness detection and higher security.…”

Section: Introductionmentioning

confidence: 99%

VeinCnnformer: convolutional neural network based transformer for vein recognition

wang,

Qin,

Zhang

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Vein recognition has received increasing attention in recent years. Currently, the deep learning such transformer show robust capacity of feature representation and successfully applied for vein recognition task. Despite of its advances in vein recognition, but it may fail to capture the 2D structure and spatial local information within each patch for image-based vision tasks. In this paper, we propose a novel Convolutional neural network based transformer for vein recognition, termed VeinCnnformer to take advantage of convolutional operations and self-attention mechanisms for improving representation learning. Our VeinCnnformer roots in the Mixed Convolutional Attention Module (MCAM), which fuses local features and global representations. Specifically, we propose three modules, convolutional neural network(CNN) module, convolutional channel attention module, and transformer module. Secondly, the convolutional channel attention module and CNN module are combined with transformer module to form the MCAM. Thirdly, we stack multiple MCAM to obtain our VeinCnnformer for vein recognition. Finally, the experimental results on three public vein databases show that our VeinCnnformer outperforms the existing vein recognition approaches and achieves the state of the art recognition results.

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Contactless finger-vein verification based on oriented elements feature

Cited by 18 publications

References 22 publications

Biometrics in the Era of COVID-19: Challenges and Opportunities

Biometrics in the Era of COVID-19: Challenges and Opportunities

Biometrics in the Era of COVID-19: Challenges and Opportunities

VeinCnnformer: convolutional neural network based transformer for vein recognition

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