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Multimodal biometrics score level fusion using non-confidence information
Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above
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
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
Multi-Modal Biometrics: Applications, Strategies and Operations
The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented
Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability
As biometric technology is increasingly deployed, it will be common to
replace parts of operational systems with newer designs. The cost and
inconvenience of reacquiring enrolled users when a new vendor solution is
incorporated makes this approach difficult and many applications will require
to deal with information from different sources regularly. These
interoperability problems can dramatically affect the performance of biometric
systems and thus, they need to be overcome. Here, we describe and evaluate the
ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007
BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion
algorithms when biometric signals were generated using several biometric
devices in mismatched conditions. Quality measures from the raw biometric data
are available to allow system adjustment to changing quality conditions due to
device changes. This system adjustment is referred to as quality-based
conditional processing. The proposed fusion approach is based on linear
logistic regression, in which fused scores tend to be log-likelihood-ratios.
This allows the easy and efficient combination of matching scores from
different devices assuming low dependence among modalities. In our system,
quality information is used to switch between different system modules
depending on the data source (the sensor in our case) and to reject channels
with low quality data during the fusion. We compare our fusion approach to a
set of rule-based fusion schemes over normalized scores. Results show that the
proposed approach outperforms all the rule-based fusion schemes. We also show
that with the quality-based channel rejection scheme, an overall improvement of
25% in the equal error rate is obtained.Comment: Published at IEEE Transactions on Systems, Man, and Cybernetics -
Part A: Systems and Human
Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen
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