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.  

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.

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.

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.

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.

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 Assessment on Extraction and Matching of Finger Vein and Iris Pattern based Authentication System

2020 ◽  
Vol 8 (5) ◽  
pp. 4182-4194
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Personal Identification ◽  
The Body ◽  
Infrared Light ◽  
Vein Pattern ◽  
Finger Vein ◽  
Line Tracking ◽  
Authentication System ◽  
Future Work

Biometrics uses human behavioral features for personal identification and has become most popular and promising alternatives than the traditional methods. The vein pattern is hidden inside the body and hence the problem of forgery in vein is consequently reduced when compared to fingerprint. Iris is one of the most reliable biometric traits due to its uniqueness and stability. The uniqueness of iris texture comes from the random and complex structures such as furrows, ridges, crypts, rings, corona, and freckles etc. which are formed during gestation. Often iris is combined with other biometric features for robust biometric systems. The finger vein pattern acquired under infrared light is used to design an accurate personal authentication system. The personal identification method based on vein extract the patterns from an unclear original image and line tracking operations with randomly varied start points are repeatedly carried out. This paper reviews various techniques introduced in finger vein and iris recognition system. This paper mainly focuses in introduction about finger vein and iris pattern, survey of existing research works done in the process under finger vein combined with iris recognition such as image acquisition, vein and iris enhancement, vein and iris pattern extraction and vein and iris pattern matching. Finally the challenges and future work are discussed in order to improve the left finger vein pattern with right iris and right finger vein pattern with left iris recognition.

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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