scholarly journals Finger Vein Recognition Using Vgg-16 Cnn Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 112-114
Keyword(s):  
Error Rate ◽  
Data Security ◽  
System Security ◽  
Biometric System ◽  
Electronic Technology ◽  
Equal Error Rate ◽  
Biometric Recognition ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

With the advancement in the electronic technology, data identification and security is to be mainly considered as a factor in the security. Biometric recognition has been taken in to consideration for security purpose. Data security has to be done to prevent the system security from transmission of data by unauthorized users. Various authentications are taken in to consideration but most commonly focuses on finger print biometric system. Biometric recognition is taken in priority which is high safe and security oriented. Preprocessing, extraction and Equal Error rate are taken in to consideration. In this we are mainly focusing in finger vein authentication domains over the system implementation.

scholarly journals Finger Vein Recognition Based on (2D)2PCA and Metric Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Gongping Yang ◽  
Xiaoming Xi ◽  
Yilong Yin
Keyword(s):  
Metric Learning ◽  
Recognition Rate ◽  
Class Imbalance ◽  
Biometric Recognition ◽  
Class Imbalance Problem ◽  
Finger Vein ◽  
Imbalance Problem ◽  
Knn Classifier ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.

scholarly journals Biometric, Fingervein, Machine L Implementation of a Security System, Using Captured Fingervein, Applying the Concepts of Machine Learning

2020 ◽  
Vol 9 (04) ◽  
pp. 24994-25007
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Akinbohun Folake ◽  
Thompson Aderonke ◽  
Korede Bashir
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Accurate Solution ◽  
Great Promise ◽  
Biometric System ◽  
Finger Vein ◽  
Similarities And Differences ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Biometric Features

This work explores the field of biometric finger vein recognition – which is the identification of individuals using the unique vein patterns under their finger skins. This work also includes the implementation of an Android fingerprint biometric system using the Android Near InfraRed (NIR) module, which exists to show the similarities and differences between the two (fingervein and fingerprint) prevalent biometric features. This work thus confirms that finger vein recognition shows great promise as an accurate solution to modern society’s problem of automated personal authentication

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

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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