Finger Vein Recognition Model for Biometric Authentication Using Intelligent Deep Learning

2021 ◽  
Author(s):  
M. V. Madhusudhan ◽  
V. Udaya Rani ◽  
Chetana Hegde
Keyword(s):  
Deep Learning ◽  
Euclidean Distance ◽  
Infrared Imaging ◽  
Short Term Memory ◽  
Extraction Process ◽  
Biometric Authentication ◽  
Biometric Data ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

In recent years, biometric authentication systems have remained a hot research topic, as they can recognize or authenticate a person by comparing their data to other biometric data stored in a database. Fingerprints, palm prints, hand vein, finger vein, palm vein, and other anatomic or behavioral features have all been used to develop a variety of biometric approaches. Finger vein recognition (FVR) is a common method of examining the patterns of the finger veins for proper authentication among the various biometrics. Finger vein acquisition, preprocessing, feature extraction, and authentication are all part of the proposed intelligent deep learning-based FVR (IDL-FVR) model. Infrared imaging devices have primarily captured the use of finger veins. Furthermore, a region of interest extraction process is carried out in order to save the finger part. The shark smell optimization algorithm is used to tune the hyperparameters of the bidirectional long–short-term memory model properly. Finally, an authentication process based on Euclidean distance is performed, which compares the features of the current finger vein image to those in the database. The IDL-FVR model surpassed the earlier methods by accomplishing a maximum accuracy of 99.93%. Authentication is successful when the Euclidean distance is small and vice versa.

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.

Real-Time Finger-Vein Recognition for Security and Monitoring System

2014 ◽  
Vol 550 ◽  
pp. 194-203
Author(s):  
S. Nandhini ◽  
D. Shyam
Keyword(s):  
Private Information ◽  
Electronic Devices ◽  
Recognition System ◽  
Biometric Authentication ◽  
Finger Vein ◽  
Security Authentication ◽  
Consumer Electronic ◽  
Authentication System ◽  
Vein Recognition ◽  
Finger Vein Recognition

— The demand for simple, convenient and high security authentication systems protecting private information is rising with the development of improved consumer electronic devices. In existing systems cards, pin numbers and passwords are used for authentication. However theft of cards and guessing of pin numbers and passwords by exploiters is a serial threat. Hence the need to protect private information by means of biometric solutions is very essential. The proposed system finger vein recognition system is a biometric authentication system. The maximum curvature method of feature extraction used here extracts the centrelines without being affected by fluctuations in vein width and brightness. The results of processing are sent using GSM to owners or administrators. The system can be used for application such as bank ATM identification and verification, automatic door locking control systems and automated attendance register system.

A finger vein recognition algorithm based on deep learning

2017 ◽  
Vol 9 (3) ◽  
pp. 220 ◽  
Author(s):  
g Chen ◽  
Zhendong Wu ◽  
Jianwu Zhang ◽  
Ping Li ◽  
Freeha Azmat
Keyword(s):  
Deep Learning ◽  
Recognition Algorithm ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

A finger vein recognition algorithm based on deep learning

2017 ◽  
Vol 9 (3) ◽  
pp. 220
Author(s):  
Freeha Azmat ◽  
Ping Li ◽  
Zhendong Wu ◽  
Jianwu Zhang ◽  
Cheng Chen
Keyword(s):  
Deep Learning ◽  
Recognition Algorithm ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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