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.
138 citations
Cites background or methods from "Biometric match score fusion using ..."
...[218] explore the application of Relevance Vector Machines (RVM) to perform score-level fusion from different classifiers....
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...[218] CASIA v4 Relevance Vector Machines (RVM) perform score-level fusion for multiple irises....
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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.
26 citations
01 Dec 2013
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.
20 citations
Cites result from "Biometric match score fusion using ..."
...As compared to other multi-modal approach, it reduces the need of multiple acquisitions using different sensors and thus lessen both transaction time and sensor cost....
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24 Nov 2016
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
01 Jan 2020
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.
16 citations
References
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10 Dec 2002
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: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale 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 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.
2,437 citations
"Biometric match score fusion using ..." refers background in this paper
...Iris recognition using a single instance may suffer from various challenges such as noisy sensor data, mis-localization, occlusion due to eyelids, effect of disease (e.g. cataract) cataract, and spoof attacks....
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TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).
Abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity These systems help achieve an increase in performance that may not be possible using a single biometric indicator Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented
1,611 citations
01 Oct 2007
TL;DR: This paper presents more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems and Fourier-based methods for solving problems in iris trigonometry and projective geometry.
Abstract: This paper presents the following four advances in iris recognition: 1) more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and ldquorotatingrdquo the eye into orthographic perspective; 3) statistical inference methods for detecting and excluding eyelashes; and 4) exploration of score normalizations, depending on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented based on 200 billion iris cross-comparisons that were generated from 632 500 irises in the United Arab Emirates database to analyze the normalization issues raised in different regions of receiver operating characteristic curves.
1,031 citations
"Biometric match score fusion using ..." refers background in this paper
...Iris recognition using a single instance may suffer from various challenges such as noisy sensor data, mis-localization, occlusion due to eyelids, effect of disease (e.g. cataract) cataract, and spoof attacks....
[...]
TL;DR: This paper addresses the problem of information fusion in verification systems and experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented.
Abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multimodal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems also help achieve an increase in performance that may not be possible by using a single biometric indicator. This paper addresses the problem of information fusion in verification systems. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are also presented.
790 citations
"Biometric match score fusion using ..." refers background in this paper
...Ross and Jain [1] presented information fusion in biometrics by combining information at score level....
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TL;DR: It is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems and an information-based data selection criterion is derived and demonstrated within this framework.
Abstract: Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.
768 citations