Finger Vein Segmentation from Infrared Images Using Spectral Clustering: An Approach for User Indentification

Vasqucz-Villar, Zenin J.; Choquehuanca-Zevallos, Juan J.; Ludeña-Choez, Jimmy; Mayhua-Lopez, Efrain

doi:10.1109/icset51301.2020.9265399

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…In 2020, Yong [ 43 ] applied a curvature algorithm for feature extraction in their FPGA system by calculating the eigenvalues of the image’s Hessian matrix. Villar et al [ 44 ] proposed the usage of Spectral Clustering (SC), in combination with a normalized Laplacian matrix and eigenvalues, to extract the vein patterns. The SC is applied on all the ROIs that are detected in the image through a mask application and the features are then used on a Logistic Regressor for classification.…”

Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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Section: Finger Vein Feature Extractionmentioning

confidence: 99%

Feature Extraction for Finger-Vein-Based Identity Recognition

et al. 2021

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“…In order to distinguish a person inherently as authorized against an impostor, biometric based personal identification systems [3] are need of an hour. Since this system identifies an individual based what he or she is rather than what he or she has.…”

Section: Introductionmentioning

confidence: 99%

A Finger Vein Security System Using DNA Encryption, LSB Data Hiding and Image Scrambling

Wagh¹,

S²,

N³

2023

Artificial Intelligence and Communication Technologies

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Finger vein technology is a secure biometric solution, reliable and non-invasive that provides authentication from fast and highly accurate identity to access data or secure areas. The personal information security in cyberspace has gradually become an important topic nowadays, and biometric identification technology has emerged as the times require. Finger vein authenticated person information security is another prominent problem in the research area. So, by combining the information security and finger vein image security will improve the security of the whole system. This paper proposes a finger vein image security system using DNA encryption, image scrambling with LSB steganography technique. The secret data of the user is first encrypted using DNA encryption. LSB data hiding is used to hide this encrypted secret data in the finger vein image. This method consists of replacing the least significant bit, the last bit of each byte, of the pixels of an image with the bits of the information to be hidden. The security of the user is given importance by encrypting the details and then hiding the information. The stego image is then encrypted using image scrambling technique. To match a user’s vein, the image is descrambled and then compared to the finger vein based on the data extracted from the image. The proposed model provides security to the user information as well as the finger vein image.

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“…Image segmentation methods have made great achievements in many fields, but some gaps still exist in finger vein segmentation [ 4 , 5 ]. Due to the poor contrast of finger vein images, current segmentation methods cannot effectively distinguish the vein from non-venous areas [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

Yang

et al. 2020

Sensors

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In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

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Finger Vein Segmentation from Infrared Images Using Spectral Clustering: An Approach for User Indentification

Cited by 3 publications

References 21 publications

Feature Extraction for Finger-Vein-Based Identity Recognition

Feature Extraction for Finger-Vein-Based Identity Recognition

A Finger Vein Security System Using DNA Encryption, LSB Data Hiding and Image Scrambling

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

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