Biometric match score fusion using RVM: A case study in multi-unit iris recognition
16 Jun 2012-pp 65-70
TL;DR: Experimental results on the CASIA-Iris-V4 Thousand database show that RVM provides better accuracy compared to single unit iris recognition and existing fusion algorithms.
Abstract: This paper presents a novel fusion approach to combine scores from different biometric classifiers using Relevance Vector Machine. RVM uses a combination of kernel functions on training data for classification and compared to SVM, it requires significantly reduced number of relevance vectors. The proposed RVM based fusion algorithm is evaluated using a case study on multi-unit iris recognition. Experimental results on the CASIA-Iris-V4 Thousand database show that RVM provides better accuracy compared to single unit iris recognition and existing fusion algorithms. With respect to SVM fusion, it is observed that, the accuracy of RVM and SVM are comparable, however, the time for RVM fusion is significantly reduced.
Citations
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TL;DR: A path forward is proposed to advance the research on ocular recognition by improving the sensing technology, heterogeneous recognition for addressing interoperability, utilizing advanced machine learning algorithms for better representation and classification, and developing algorithms for ocular Recognition at a distance.
Abstract: Display Omitted A literature review of ocular modalities such as iris and periocular is presented.Information fusion approaches that combine ocular modalities with other modalities are reviewed.Future research directions are presented on sensing technologies, algorithms, and fusion approaches. Biometrics, an integral component of Identity Science, is widely used in several large-scale-county-wide projects to provide a meaningful way of recognizing individuals. Among existing modalities, ocular biometric traits such as iris, periocular, retina, and eye movement have received significant attention in the recent past. Iris recognition is used in Unique Identification Authority of India's Aadhaar Program and the United Arab Emirate's border security programs, whereas the periocular recognition is used to augment the performance of face or iris when only ocular region is present in the image. This paper reviews the research progression in these modalities. The paper discusses existing algorithms and the limitations of each of the biometric traits and information fusion approaches which combine ocular modalities with other modalities. We also propose a path forward to advance the research on ocular recognition by (i) improving the sensing technology, (ii) heterogeneous recognition for addressing interoperability, (iii) utilizing advanced machine learning algorithms for better representation and classification, (iv) developing algorithms for ocular recognition at a distance, (v) using multimodal ocular biometrics for recognition, and (vi) encouraging benchmarking standards and open-source software development.
103 citations
Cites background or methods from "Biometric match score fusion using ..."
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TL;DR: The proposed iGRVM which incorporates incremental and granular learning in RVM can be a good alternative for biometric score classification with faster testing time.
Abstract: This paper focuses on extending the capabilities of relevance vector machine which is a probabilistic, sparse, and linearly parameterized classifier. It has been shown that both relevance vector machine and support vector machine have similar generalization performance but RVM requires significantly fewer relevance vectors. However, RVM has certain limitations which limits its applications in several pattern recognition problems including biometrics such as (1) slow training process, (2) difficult to train with large training samples, and (3) may not be suitable to handle large class imbalance. To address these limitations, we propose iGRVM which incorporates incremental and granular learning in RVM. The proposed classifier is evaluated in context to multimodal biometrics score classification using the NIST BSSR1, CASIA-Iris-Distance V4, and Biosecure DS2 databases. The experimental analysis illustrates that the proposed classifier can be a good alternative for biometric score classification with faster testing time. HighlightsThe proposed iGRVM incorporates incremental and granular learning in RVM.Experiments are performed on NIST BSSR1, CASIA-Iris-Distance V4, and Biosecure DS2 databases.Results illustrate that iGRVM can be a good alternative for biometric score classification.
22 citations
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TL;DR: In this article, the authors present an overview of the multi-biometric score-level fusion problem, along with the proposed solution in the literature, and a discussion is made to provide a clearer view of future developments especially under the identification scenario where many related applications are rapidly growing.
Abstract: Multi-biometrics is the use of multiple biometric recognition sources to provide a more dependable verification or identification decision. Fusion of multi-biometric sources can be performed on different levels, such as the data, feature, or score level. This work presents an overview of the multi-biometric score-level fusion problem, along with the proposed solution in the literature. A discussion is made to provide a comparison between multi-biometric fusion in both scenarios. This discussion aims at providing a clearer view of future developments especially under the identification scenario where many related applications are rapidly growing such as forensics and ubiquitous surveillance.
16 citations
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TL;DR: This work proposes a reliable two-stage multi-instance finger vein recognition system based on minutiae matching method by integrating a unified minutia alignment and pruning approach using Genetic algorithm and the k-modified Hausdorff distance measurement.
Abstract: Among the various multi-modal biometric approaches, multi-instance biometric appears to be understudied despite it inherits the merits of multimodal biometrics system. Multi-instance biometrics is useful when the signal quality is too low for robust verification. As compared to other multi-modal approach, multi-instance fusion reduces the need of multiple acquisitions using different sensors and thus lessen both transaction time and sensor cost. In this work, we propose a reliable two-stage multi-instance finger vein recognition system based on minutiae matching method by integrating a unified minutia alignment and pruning approach using Genetic algorithm and the k-modified Hausdorff distance (k-MHD) measurement. The proposed method is evaluated by using the SDUMLA-HMT Finger Vein database. Experiments show the proposed method is able to attain promising recognition rate compared to its single biometrics counterpart. The best result is achieved by applying the k-nearest neighbor measurement alongside, where the recognition rate can be up to 99.7% when MHD is used for matching.
16 citations
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TL;DR: In this paper, a multimodal biometric system uses more than one biometric trait or modality for recognition of an individual, which fuses different types of input at different levels: Score level, Feature level and Decision level.
Abstract: Human identification systems based on biometrics are used in many applications to increase the security level. There are different biometric traits which are used in various applications. Monomodal biometric systems face many challenges such as error rates, using only single biometric for human recognition. Today, to increase the security of the authentication system, various multimodal biometric systems are proposed. A multimodal biometric system uses more than one biometric trait or modality for recognition of an individual. Multimodal biometric systems fuses different types of input at different level: Score level, Feature level and Decision level to get the better performance of the system.
12 citations
References
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
38,164 citations
"Biometric match score fusion using ..." refers methods in this paper
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TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Abstract: We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
5,535 citations
"Biometric match score fusion using ..." refers background in this paper
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TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Abstract: This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the 'relevance vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art 'support vector machine' (SVM) We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages These include the benefits of probabilistic predictions, automatic estimation of 'nuisance' parameters, and the facility to utilise arbitrary basis functions (eg non-'Mercer' kernels) We detail the Bayesian framework and associated learning algorithm for the RVM, and give some illustrative examples of its application along with some comparative benchmarks We offer some explanation for the exceptional degree of sparsity obtained, and discuss and demonstrate some of the advantageous features, and potential extensions, of Bayesian relevance learning
5,059 citations
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TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.
2,770 citations
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