A review of finger vein biometrics authentication System

Daas, Sara; Boughazi, Mohamed; Sedhane, Mouna; Bouledjfane, Badreddine

doi:10.1109/icass.2018.8652005

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…2) is based on a Raspberry Pi PI3. 5 The communication with the scanner happens through Ethernet, USB or WiFi. The scanning of the finger is made via infrared lights and three cameras placed with different angles.…”

Section: Implementation Resultsmentioning

confidence: 99%

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$$\mathsf {BioLocker}$$: A Practical Biometric Authentication Mechanism Based on 3D Fingervein

Durak

Huguenin-Dumittan

Vaudenay

2020

Applied Cryptography and Network Security

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We design a consecution of protocols which allows organizations to have secure strong access control of their users to their desktop machines based on biometry. It provides both strong secure authentication and privacy. Moreover, our mechanism allows the system admins to grant a various level of access to their end-users by fine tuning access control policy. Our system implements privacy-by-design. It separates biometric data from identity information. It is practical: we fully implemented our protocols as a proof of concept for a hospital. We use a 3D fingervein scanner to capture the biometric data of the user on a Raspberry Pi. For the biometry part, we developed an optimal way to aggregate scores using sequential distinguishers. It trades desired FAR and FRR against an average number of biometric captures.

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Section: Implementation Resultsmentioning

confidence: 99%

“…Previous work. While not as widely deployed as fingerprint authentication, fingervein recognition has been a hot topic in recent years and many systems have been proposed [20,18,5]. In 2015, Wang et al [24] showed how hand-vein recognition can be used to build a practical physical access control system.…”

Section: Introductionmentioning

confidence: 99%

$$\mathsf {BioLocker}$$: A Practical Biometric Authentication Mechanism Based on 3D Fingervein

Durak

Huguenin-Dumittan

Vaudenay

2020

Applied Cryptography and Network Security

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“…This is because the light in a range of 700-1000 nm is strongly absorbed by oxidized haemoglobin in veins [2] and lowly by the tissues [3]. This method of identification has been already described well in rich literature [4].…”

Section: Introductionmentioning

confidence: 99%

Modified Distance Transformation for Image Enhancement in NIR Imaging of Finger Vein System

Bernacki

Moroń

2020

Sensors

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Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over 90 % identification accuracy for all analyzed datasets.

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Deep semantic segmentation-based unlabeled positive CNN’s loss function for fully automated human finger vein identification

Al-Azzawi

2023

AIP Conference Proceedings

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A review of finger vein biometrics authentication System

Cited by 7 publications

References 24 publications

$$\mathsf {BioLocker}$$: A Practical Biometric Authentication Mechanism Based on 3D Fingervein

$$\mathsf {BioLocker}$$: A Practical Biometric Authentication Mechanism Based on 3D Fingervein

Modified Distance Transformation for Image Enhancement in NIR Imaging of Finger Vein System

Deep semantic segmentation-based unlabeled positive CNN’s loss function for fully automated human finger vein identification

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