Fusion at decision level in multimodal biometric authentication system using Iris and Finger Vein with novel feature extraction

2014 ◽  
Author(s):  
Sudhamani M J ◽  
M K Venkatesha ◽  
Radhika K R
Keyword(s):  
Feature Extraction ◽  
Biometric Authentication ◽  
Decision Level ◽  
Finger Vein ◽  
Authentication System

scholarly journals Multimodal Authentication of Ocular Biometric and Finger Vein Verification in Smartphones: A Review

2018 ◽  
Vol 7 (3.12) ◽  
pp. 161
Author(s):  
Dheeraj Hebri ◽  
Vasudeva .
Keyword(s):  
Feature Extraction ◽  
Biometric Authentication ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Finger Vein ◽  
The Future ◽  
Authentication System ◽  
Future Work ◽  
Feature Extraction And Matching

Biometric authentication has demanded a lot of attention from the researchers in the current age, as the field aimsto identify human behavioral charcteristics based on fingerprint, finger vein, ocular, face, palm, etc. So, this field is useful in many applications for offering security and authentication of industry or business. Also, the multimodal biometric system is used to provide a greater security and higher reliability that combines two or more biometric identifiers. Finger vein and ocular-based multimodal biometric authentication system are one of the major techniques which have been considered for efficient identification and verification purpose. This system mainly works in some common stages which include, scanning of finger vein and ocular, pre-processing, feature extraction and matching of finger vein and ocular in a database as well. This paper attempts to review various recent and advanced multimodal finger vein and ocular biometric authentication systems. Finally, possible directions in the multimodal biometric authentication system for the future work are also discussed.  

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.

Score-Level Multimodal Biometric Authentication of Humans Using Retina, Fingerprint, and Fingervein

2020 ◽  
Vol 11 (3) ◽  
pp. 20-30
Author(s):  
Rohit Srivastava
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Biometric Authentication ◽  
Fundamental Idea ◽  
Automated Teller Machines ◽  
Finger Vein ◽  
Combination Approach ◽  
Test Result ◽  
Modular Framework

This paper characterizes a multi-modular framework for confirmation, dependent on the biometric combination of retina, finger vein, and unique mark acknowledgment. The authors have proposed feature extraction in retina acknowledgment model by utilizing SIFT and MINUTIA. Security is the fundamental idea in ATM (Automated Teller Machines) today. The use of multi-modular biometrics can be ATM. The work includes three biometric attributes of a client to be specific retina, unique mark, and finger veins. These are pre-prepared and joined (fused) together for score level combination approach. Retina is chosen as a biometric attribute as there are no parallel retina feature matches except if they are of the comparative client; likewise, retina has a decent vessel design making it a decent confirming methodology when contrasted with other biometric attributes. Security is found in the framework by multi-modular biometric combination of retina with finger vein and unique finger impression. Feature extraction approach and cryptography are utilized so as to accomplish security. The element extraction is finished with the assistance of MINUTIA and SIFT calculation, which are at that point characterized utilizing deep neural network (DNN). The element key focuses are intertwined at score level utilizing separation normal and later matched. The test result assessed utilizing MATLAB delineates the significant improvement in the presentation of multi-modular biometric frameworks with higher qualities in GAR and FAR rates.

Biometric authentication system using EEG biometric trait – A review

2021 ◽  
Author(s):  
Fatin Atiqah Rosli ◽  
Saidatul Ardeenawatie Awang ◽  
Azian Azamimi Abdullah ◽  
Mohammad Shahril Salim
Keyword(s):  
Biometric Authentication ◽  
Authentication System ◽  
Biometric Trait

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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