AN INTELLIGENT CLASSIFIER FUSION TECHNIQUE FOR IMPROVED MULTIMODAL BIOMETRIC AUTHENTICATION USING MODIFIED DEMPSTER-SHAFER RULE OF COMBINATION

Multimodal biometric technology relatively is a technology developed to overcome those limitations imposed by unimodalbiometric systems. The paradigm consolidates evidence from multiple biometric sources offering considerableimprovements in reliability with reasonably overall performance in many applications. Meanwhile, the issue of efficient andeffective information fusion of these evidences obtained from different sources remains an obvious concept that attractsresearch attention. In this research paper, we consider a classical classifier fusion technique, Dempster’s rule of combinationproposed in Dempster-Shafer Theory (DST) of evidence. DST provides useful computational scheme for integratingaccumulative evidences and possesses the potential to update the prior every time a new data is added in the database.However, it has some shortcomings. Dempster Shafer evidence combination has this inability to respond adequately to thefusion of different basic belief assignments (bbas) of evidences, even when the level of conflict between sources is low. Italso has this tendency of completely ignoring plausibility in the measure of its belief. To solve these problems, this paperpresents a modified Dempster’s rule of combination for multimodal biometric authentication which integrates hyperbolictangent (tanh) estimators to overcome the inadequate normalization steps done in the original Dempster’s rule ofcombination. We also adopt a multi-level decision threshold to its measure of belief to model the modified Dempster Shaferrule of combination.Keywords: Information fusion, Multimodal Biometric Authentication, Normalization technique, Tanh Estimators

A novel scheme to address the fusion uncertainty in multi-modal continuous authentication schemes on mobile devices

Interest in continuous mobile authentication schemes has increased in recent years. These schemes use sensors on mobile devices to collect the biometric data about a user. The use of multiple sensors in a multi-modal scheme has been shown to improve the accuracy. However, sensor scores are often combined using simplistic techniques such as averaging. To date, the effect of uncertainty in score fusion has not been explored. In this paper, we present a novel Dempster-Shafer based score fusion approach for continuous authentication schemes. Our approach combines the sensor scores factoring in the uncertainty of the sensor. We propose and evaluate five techniques for computing uncertainty. Our proof-of-concept system is tested on three state-of-the-art datasets and compared with common fusion techniques. We find that our proposed approach yields the highest accuracies compared to the other fusion techniques and achieves equal error rates as low as 8.05%

Dual iris authentication system using dezert smarandache theory

In this paper, a dual iris authentication using Dezert Smarandache theory is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract only half iris disc that contains relevant information and is less affected by noise. For that, a Hough transform is used. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris template. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The score fusion is applied using DSmC combination rule. The proposed method has been tested on a subset of iris database CASIA-IrisV3-Interval. The obtained results give a satisfactory performance with accuracy of 99.96%, FAR of 0%, FRR of 3.89%, EER of 2% and processing time for one iris image of 12.36 s

Unimodal and multimodal biometric sensing systems : a review

Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2017Electrical, Electronic and Computer Engineerin

A Review Of Multilevel Multibiometric Fusion System

Biometric systems allow automatic person recognition and authenticate based on the physical or behavioral characteristic. In recent years, researchers have paid close attention to the design of efficient multi-modal biometric systems due to their ability to withstand spoof attacks. Sometimes single biometric traits fail to extract relevant information for verifying the identity of a person. Therefore, combining multiple modalities, enhanced performance reliability could be achieved. If the security level increases then multi-level fusion techniques are used. This paper discusses the many fusion levels: algorithms, level of fusion, methods used for integrating the multiple verifiers and their applications

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 themThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of thisthis work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers