scholarly journals Generalized net model of a biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations

2020 ◽  
Vol 26 (4) ◽  
pp. 71-79
Author(s):  
Zhelyana Ivanova ◽  
◽  
Veselina Bureva ◽  
Keyword(s):  
Access Control ◽  
Research Work ◽  
Biometric Authentication ◽  
Biometric System ◽  
The Real ◽  
Multimodal Biometric System ◽  
Intuitionistic Fuzzy ◽  
Authentication System ◽  
Generalized Net ◽  
Palm Vein

In the current research work a multimodal biometric system is investigated. It combines the palm vein authentication and palm geometry recognition methods. The system will be used to manage the access control. The apparatus of generalized nets is applied to model the biometric authentication processes. The constructed generalized net model of biometric authentication system based on palm geometry and palm vein matching using intuitionistic fuzzy evaluations can be used for simulation of the real processes. The intuitionistic fuzzy evaluations are used to compare the user traits with the templates stored in database.

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.  

Multimodal Biometric System

2011 ◽  
pp. 478-485 ◽  
Author(s):  
Ajita Rattani
Keyword(s):  
Access Control ◽  
Personal Identification ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Personal Authentication

Personal identification is a fundamental activity within our society. This identification is made possible by the emergence of the new concept of biometrics. Biometrics is the science of identifying or verifying an individual based on the physiological or behavioral characteristics like face, fingerprint, iris, signature, voice, retina, handwriting, and so forth. Biometric identifiers for personal authentication reduce or eliminate reliance on tokens, PINs, and passwords. It can be integrated into any application that requires security, access control, and identification or verification of people (Jain, Ross, & Prabhakar, 2004).

scholarly journals Multi-Model Biometric Authentication System for Smart Attendance

2020 ◽  
Vol 8 (6) ◽  
pp. 4284-4287
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Raspberry Pi ◽  
Machine Learning Algorithm ◽  
Biometric Authentication ◽  
Biometric System ◽  
Authentication System ◽  
Paper Method

To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.

scholarly journals A Robust Multimodal Biometric System VIA Multiple Svms

2019 ◽  
Vol 8 (11S) ◽  
pp. 203-209
Keyword(s):  
Support Vector ◽  
Biometric Authentication ◽  
Biometric System ◽  
Multimodal Biometrics ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
Average Accuracy ◽  
Vector Machines ◽  
Final Score ◽  
Matching Score

Generally single Support Vector Machine (SVM) is employed in existing multimodal biometric authentication techniques, and it assumes that whole set of the classifiers is available. But sometimes it is not possible due to some circumstances e.g. injury, some medical treatment etc. This paper includes a robust multimodal biometric authentication system that integrates FKP (Finger-Knuckle Print), face and fingerprint at matching score level fusion using multiple parallel Support Vector Machines (SVMs). Multiple SVMs are applied to overcome the problem of missing biometric modality. Every possible combination of three modalities (FKP, face and fingerprint) are taken into consideration and all combinations have a corresponding SVM to fuse the matching scores and produce the final score set for decision making. Proposed system is more flexible and robust as compared to existing multimodal biometric system with single SVM. The average accuracy of proposed system is estimated on a publicly available dataset with the use of MUBI tool(Multimodal Biometrics Integration tool) and MATLAB 2017b.

scholarly journals Multimodal Biometric Authentication for Smartphones

2021 ◽  
Vol 9 (12) ◽  
pp. 1559-1568
Author(s):  
Mrunal Pathak
Keyword(s):  
Multimodal Fusion ◽  
Biometric Identification ◽  
Identification System ◽  
Sensitive Information ◽  
Biometric Authentication ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Biometric Systems ◽  
Real Time Applications ◽  
Eye Recognition

Abstract: Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

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