2,242 research outputs found
Multimodal biometric system for ECG, ear and iris recognition based on local descriptors
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Combination of multiple information extracted from different biometric modalities in multimodal biometric recognition system aims to solve the different drawbacks encountered in a unimodal biometric system. Fusion of many biometrics has proposed such as face, fingerprint, iris…etc. Recently, electrocardiograms (ECG) have been used as a new biometric technology in unimodal and multimodal biometric recognition system. ECG provides inherent the characteristic of liveness of a person, making it hard to spoof compared to other biometric techniques. Ear biometrics present a rich and stable source of information over an acceptable period of human life. Iris biometrics have been embedded with different biometric modalities such as fingerprint, face and palm print, because of their higher accuracy and reliability. In this paper, a new multimodal biometric system based ECG-ear-iris biometrics at feature level is proposed. Preprocessing techniques including normalization and segmentation are applied to ECG, ear and iris biometrics. Then, Local texture descriptors, namely 1D-LBP (One D-Local Binary Patterns), Shifted-1D-LBP and 1D-MR-LBP (Multi-Resolution) are used to extract the important features from the ECG signal and convert the ear and iris images to a 1D signals. KNN and RBF are used for matching to classify an unknown user into the genuine or impostor. The developed system is validated using the benchmark ID-ECG and USTB1, USTB2 and AMI ear and CASIA v1 iris databases. The experimental results demonstrate that the proposed approach outperforms unimodal biometric system. A Correct Recognition Rate (CRR) of 100% is achieved with an Equal Error Rate (EER) of 0.5%
Feature Level Fusion of Face and Fingerprint Biometrics
The aim of this paper is to study the fusion at feature extraction level for
face and fingerprint biometrics. The proposed approach is based on the fusion
of the two traits by extracting independent feature pointsets from the two
modalities, and making the two pointsets compatible for concatenation.
Moreover, to handle the problem of curse of dimensionality, the feature
pointsets are properly reduced in dimension. Different feature reduction
techniques are implemented, prior and after the feature pointsets fusion, and
the results are duly recorded. The fused feature pointset for the database and
the query face and fingerprint images are matched using techniques based on
either the point pattern matching, or the Delaunay triangulation. Comparative
experiments are conducted on chimeric and real databases, to assess the actual
advantage of the fusion performed at the feature extraction level, in
comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics
Biometric systems have to address many requirements, such as large population
coverage, demographic diversity, varied deployment environment, as well as
practical aspects like performance and spoofing attacks. Traditional unimodal
biometric systems do not fully meet the aforementioned requirements making them
vulnerable and susceptible to different types of attacks. In response to that,
modern biometric systems combine multiple biometric modalities at different
fusion levels. The fused score is decisive to classify an unknown user as a
genuine or impostor. In this paper, we evaluate combinations of score
normalization and fusion techniques using two modalities (fingerprint and
finger-vein) with the goal of identifying which one achieves better improvement
rate over traditional unimodal biometric systems. The individual scores
obtained from finger-veins and fingerprints are combined at score level using
three score normalization techniques (min-max, z-score, hyperbolic tangent) and
four score fusion approaches (minimum score, maximum score, simple sum, user
weighting). The experimental results proved that the combination of hyperbolic
tangent score normalization technique with the simple sum fusion approach
achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201
Person Verification Based on Multimodal Biometric Recognition
Nowadays, person recognition has received significant attention due to broad applications
in the security system. However, most person recognition systems are implemented
based on unimodal biometrics such as face recognition or voice recognition. Biometric
systems that adopted unimodal have limitations, mainly when the data contains outliers
and corrupted datasets. Multimodal biometric systems grab researchers’ consideration
due to their superiority, such as better security than the unimodal biometric system and
outstanding recognition efficiency. Therefore, the multimodal biometric system based on
face and fingerprint recognition is developed in this paper. First, the multimodal biometric
person recognition system is developed based on Convolutional Neural Network (CNN)
and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused
by using match score level fusion based
on Weighted Sum-Rule. The verification
process is matched if the fusion score is
greater than the pre-set threshold t. The
algorithm is extensively evaluated on UCI
Machine Learning Repository Database
datasets, including one real dataset with
state-of-the-art approaches. The proposed
method achieves a promising result in the
person recognition system
A Survey of Biometric Recognition Systems in E-Business Transactions
The global expansion of e-business applications has introduced novel challenges, with an escalating number of security issues linked to online transactions, such as phishing attacks and identity theft. E-business involves conducting buying and selling activities online, facilitated by the Internet. The application of biometrics has been proposed as a solution to mitigate security concerns in e- business transactions. Biometric recognition involves the use of automated techniques to validate an individual's identity based on both physiological and behavioural characteristics. This research focuses specifically on implementing a multimodal biometric recognition system that incorporates face and fingerprint data to enhance the security of e-business transactions. In contrast to unimodal systems relying on a single biometric modality, this approach addresses limitations such as noise, universality, and variations in both interclass and intraclass scenarios. The study emphasizes the advantages of multimodal biometric systems while shedding light on vulnerabilities in biometrics within the e- business context. This in-depth analysis serves as a valuable resource for those exploring the intersection of e-business and biometrics, providing insights into the strengths, challenges, and best practices for stakeholders in this domain. Finally, the paper concludes with a summary and outlines potential avenues for future research
- …