Multimodal Biometric Authentication Research Articles

A New Framework for Match on Card and Match on Host Quality Based Multimodal Biometric Authentication

Smart cards are widely used to deploy secure and cost effective identity management systems. Integration of biometrics into the smart card leads to a strong two-factor authentication system through the match on card (MOC) process. Since MOC uses fixed authentication strategies during the life cycle of smart card, this leads to a low performance and high failure to acquire error in uncontrolled noisy environments. To solve this problem, this paper proposes a sequential quality based framework for biometric authentication. In the proposed framework a set of classifiers have been used to manage the workflow of the framework based on the quality of samples. Accordingly, subjects can be dynamically authenticated using MOC and MOH. A multimodal chimera database is used to evaluate this framework. Our findings indicate that the proposed approach provides higher accuracy than the unimodal MOC and MOH by 11.29% and 5.12%, respectively. Furthermore, the proposed framework can authenticate 83.85% of users without auxiliary trait at the expense of only 1.21% lower accuracy compared to parallel fusion, which require acquisition of all traits for entire users. Analysis of the results demonstrates that the proposed approach provides a compromise between accuracy, user convenience, security and system complexity.

Secure Transmission of Medical Images Using Elliptic Curve Cryptography with Multimodal Biometric Authentication

Secure Transmission of Medical Images Using Elliptic Curve Cryptography with Multimodal Biometric Authentication

Multibiometric authentication with MATLAB simulation

Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.

Multiple classifiers in biometrics. part 1: Fundamentals and review

We provide an introduction to Multiple Classifier Systems (MCS) including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods. This introduction complements other existing overviews of MCS, as here we also review the most prevalent theoretical framework for MCS and discuss theoretical developments related to MCS.The introduction to MCS is then followed by a review of the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. This review includes general descriptions of successful MCS methods and architectures in order to facilitate the export of them to other information fusion problems.Based on the theory and framework introduced here, in the companion paper we then develop in more technical detail recent trends and developments in MCS from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in the present paper, methods in the companion paper are introduced in a general way so they can be applied to other information fusion problems as well. Finally, also in the companion paper, we discuss open challenges in biometrics and the role of MCS to advance them.

Multiple classifiers in biometrics. Part 2: Trends and challenges

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key role.

A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding

A secure Multimodal Biometric Authentication with Cryptographic key Management Using Double Random Phase Encoding

Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

A multimodal biometric system is applied to recognize individuals as authentication, identification and verification for claimed identity. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra-class variations, inter-class variations, non universality etc. In this paper a multi modal biometric algorithm is designed by integrating iris, palm print, face and signature based on encoded discrete wavelet transform for image analysis and authentication. Multi level wavelet based fusion approach is applied, integrated and encoded into single composite image for matching decision. It reduces the memory size, increases the recognition accuracy and ERR using multimodal biometric approach when compared to individual biometric traits. The complexity of fusion and the reconstruction algorithm is suitable for many real time applications.

Design and Modeling of Real Time Multimodal Biometric Authentication System

Authentication is a major challenge for accessing different web-based applications, information exchange, communicating people, conducting e-business and performing financial operations, access to system like ATM, Wi-Fi etc. Biometrics based authentication could be used to prevent unauthorized access to these systems. Multi-modal biometric authentication system could be used to further strengthen the security by overcoming the limitations imposed by unimodal biometric authentication system. This paper aimed to design and model the multimodal biometric authentication system for secured access to different web applications through WLAN. The proposed system is designed using UML (Unified Modelling Language) diagrams.

A Multimodal Biometric Authentication Technique using Fused Features of Finger, Palm and Speech

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Multimodal biometric identification system based on finger‐veins using hybrid rank–decision‐level fusion technique

The highly random manner in which veins spread along a finger, their immunity to counterfeiting, active liveness, and user friendliness make finger veins the best choice for a biometric identification system (BIS). In this paper, veins of six fingers of two hands of a person are used to develop a secure, reliable, and robust multimodal BIS (MBIS). The main structure of the proposed MBIS is based on the effective combination of rank‐ and decision‐level fusion. In the training step, the power (weight) of each single modality is estimated by extracting the information that lies in the cumulative match characteristic (CMC) curve. The testing step consists of two main parts. In the first part, the region of the finger vein is extracted by using a simple method, and then the binarized statistical image features (BSIFs) algorithm is used to extract feature vectors. In the second part, final decision for the test input probe is made by generating ‘top rank‐decision matrix’, which fuses the information of each biometric identifier in the hybrid rank‐decision level. The obtained results show that proposed method is more reliable and accurate than other fusion techniques at the post‐classification fusion level. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Biometric analysis using fused feature set from side face texture and electrocardiogram

Multimodal biometric authentication requires fusion of information extracted from different biometric modalities. Face recognition is the most common and versatile biometric parameter used for years. Recently, biosignals such as electrocardiogram (ECG), photoplathysmogram etc. are under study for probable use in authentication work. It is also established that multi-parameter approach in biometric analysis plays a vital role in increasing accuracy and robustness and preventing spoofing in spite of more computational demand. In the present study, information fusion based authentication system is proposed using face and ECG. Instead of conventional face image, a unique frontal face textural signal is proposed. This leads to simpler data processing similar to that of ECG signal. Finally, information from both the signals is fused at mother template generation level. A good accuracy is achieved using mean square deviation method as presented in the result section. A stability study is also made with five volunteers to check the long term variability of the features.

Multi-Modal Biometric Authentication and Secure Transaction Operation Framework for E-Banking

The rationale of the research work is to suggest a multi-modal biometric authentication and secure transaction operation framework for E-Banking. The literature survey identifies the various types of E-Banking Channels available as on-date, the various types of biometric technologies available as on-date as well the significant metrics affecting their performance while deploying them in various different e-banking channels. The performance analysis of various types of biometric technologies based on significant metrics for Biometrics Implementation further identifies the currently implementable biometric technologies for the various different e-banking channels. Subsequently a requirement analysis of potential e-banking channels is followed by System Suitability Analysis to identify which multi-biometrics and support mechanisms are suitable for particular e-banking channels. The final conclusion suggests a viable multi-modal biometric authentication and secure transaction operation framework for various e-banking channels.

Multi Modal Biometric Security for Mobile Ad-hoc Networks and Its Applications

Objectives: We have proposed a plan of blending multimodal biometric confirmation and interruption identification to enhance the security model in Mobile Adhoc Networks. Methods/Statistical analysis: Technicalities basically based arrangement of principles is used by the unique mark processor, which offers overhauls in general execution. Face acknowledgment is another most recent progression in biometrics. Iris acknowledgment is additionally utilized as a part of verification procedure. To upgrade upon this idea, Dempster–Shafer idea has been utilized for IDS and sensor combination considering more than one apparatus is utilized at on each event opening. The device goes to a choice regardless of whether man verification (or IDS) is required and which biosensors (or IDS) must be chosen, depending on the security wished. The decisions are made in a completely dispersed way by each confirmation apparatus and IDS. Findings: The multi model biometric verification plans and interruption recognition for MANETs from the demeanor of an armed force situation. Joining consistent verification and interruption discovery might be a powerful strategy to improve the security general execution in high wellbeing MANETs. Application/Improvements: In this proposed plan blast the high well being in armed force utility and likewise utilized as a part of security reason in managing an account area.

An Efficient Multimodal Biometric Authentication Integrating Fingerprint and Face Features

Biometrics authentication has been used in many applications such as e-commerce, access control etc. In this research, multimodal biometric authentication in light of score level combination of fingerprint and face is proposed. Face and fingerprint is two of the most popular biometric traits and can complement each other for more reliable user authentication. To verify the effectiveness of the proposed method, FERET, SDUMLA-HMT and FVC2002 databases are used in this research. Furthermore, experimental results showed that the proposed fusion based method for fingerprint and face recognition can improve the performance in terms of Equal Error Rate (EER). Eventually, the experimental result shows that our proposed fusion based technique appears to be promising with 7.01~9.13% of EER based on the different datasets.

Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System

Perfectly segmenting the area of the iris is one of the most important steps in iris recognition. There are several problematic areas that affect the accuracy of the iris segmentation step, such as eyelids, eyelashes, glasses, pupil (due to less accurate iris segmentation), motion blur, and lighting and specular reflections. To solve these problems, gradient magnitude and Fourier descriptor are employed to do iris segmentation in the proposed Multimodal Biometric Authentication System (MBAS). This approach showed quite promising results, i.e. an accuracy rate of 97%. The result of the iris recognition system was combined with the result of an open-source fingerprint recognition system to develop a multimodal biometrics authentication system. The results of the fusion between iris and fingerprint authentication were 99% accurate. Data from Multimedia Malaysia University (MMUI) and our own prepared database, the SGU-MB-1 dataset, were used to test the accuracy of the proposed system.

Two Levels Fusion Based Multimodal Biometric Authentication Using Iris and Fingerprint Modalities

In the document, we are intending to present an innovative technique for the multimodal biometric authentication. Initially the input image is preprocessed then offered to feature extraction, where the modified local binary pattern is effectively utilized. Thereafter, the extracted features are furnished to the feature level and score level fusions. In feature level fusion, extracted features are offered to the GSO where the optimal features are shortlisted, and are furnished to the optimized neural network which effectively detects the iris and fingerprint image. In score level fusion, extracted features from the iris image are offered to the PSO and naive bayes classifier here one score value is achieved. After that, extracted features from the fingerprint image are applied to the AGFS and then one score value is attained. Finally, both the score values are combined. The evaluation tools utilized precision, FAR and FRR. The proposed method implemented in MATLAB platform.

Estimation of Performance in Multimodal Biometric based Authentication System using Various Clustering

Objectives: A swift growth of awareness in biometric based applications can be currently seen in the world. It includes different approaches to solve problems at hand. Methods: In this paper, Rough set with different classifiers are discussed to improve the performance of biometric system based authentication. This paper presents software systems based on rough sets comparing performance evaluation with different classifier techniques, fusion methods, filtering techniques and it provide sophisticated graphical environment. The decision fusion approach of multi modal biometric authentication gives the excellent performance in security system. Findings: A special place among different extensions is taken by the loom that replaces imperceptible relation. In the point of many simplification techniques, variant and extension of rough sets in a consistent appearance of the theory and implementation methodology is in place. This paper is meant to fulfil the specified necessitates and various challenges available in multimodal biometric authentication systems. Applications: This is used in National Secure the borders, Organizational/Enterprise, Identity and access management, Personal, Preventing impersonation (ID theft).

Extending identity management system with multimodal biometric authentication

Techniques for authentication that are used in today's identity management systems are vulnerable when they are used over the network. In order to prevent fraud and unauthorized data access, it is important to ensure the identity of the person who submitted authentication credentials. The authentication process can be additionally secured by using biometric data for user verification. Moreover, precision of biometric authentication can be improved by the use of multimodal biometrics. This paper presents a system which has been designed for identity management based on FreeIPA solution for digital identity management and MMBio framework for multimodal biometrics. Proposed system provides multifactor authentication, where MMBio framework is used for handling user biometric data. Developed prototype confirms possible integration of identity management and multimodal biometric systems.

An Efficient Human Identification through MultiModal Biometric System

Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.

Score Level Fusion Based Bimodal Biometric Identification Using Thepade's Sorted n-ary Block Truncation Coding with Variod Proportions of Iris and Palmprint Traits

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Multimodal biometrics such as palmprints and iris provide highly effective automatic mechanisms for use in personal identification and helps to minimize the system error rate. This paper presents a new method for extracting features in spatial domain from palmprints and iris. Thepade's Block Truncation Coding using level 2 is taken here to reduce the feature vector size. For improving accuracy in form of genuine acceptance rate(GAR)in multimodal biometric identification we take iris and palmprint together at matching score level. The test beds of 60 pairs of Iris and Palmprint samples of 10 person (6 per person of iris as well as Palmprint) are used as test bed for experimentation. In this Paper different color spaces are considered on iris images for improvement in genuine acceptance ratio (GAR). Experimental results consider different matching score proportion of Iris: Palmprint. Using propose techniques with Iris: Palmprint Score 15:1 using TSTBTC Level2 given better performance as indicated by higher GAR values than all other considers scores. RGB color spaces for multimodal fusion of Iris: Palmprint gives high GAR than all other color spaces like YCgCb, YIQ, YCbCr, YIQ, and KLUV.