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 novel scheme for score-level fusion based on weighted quasi-arithmetic mean (WQAM) has been proposed, which outperforms the previously proposed score fusion rules based on transformation, classification and density estimation methods.
Abstract: Biometrics is now being principally employed in many daily applications ranging from the border crossing to mobile user authentication. In the high-security scenarios, biometrics require stringent accuracy and performance criteria. Towards this aim, multi-biometric systems that fuse the evidences from multiple sources of biometric have exhibited to diminish the error rates and alleviate inherent frailties of the individual biometric systems. In this article, a novel scheme for score-level fusion based on weighted quasi-arithmetic mean (WQAM) has been proposed. Specifically, WQAMs are estimated via different trigonometric functions. The proposed fusion scheme encompasses properties of both weighted mean and quasi-arithmetic mean. Moreover, it does not require any leaning process. Experimental results on three publicly available data sets (i.e. NIST-BSSR1 Multimodal, NIST-BSSR1 Fingerprint and NIST-BSSR1 Face) for multi-modal, multi-unit and multi-algorithm systems show that presented WQAM fusion algorithm outperforms the previously proposed score fusion rules based on transformation (e.g. t
-norms), classification (e.g. support vector machines) and density estimation (e.g. likelihood ratio) methods.
15 citations
23 Mar 2015
TL;DR: A novel context switching algorithm that dynamically selects the best descriptor for color iris and periocular regions is proposed that is evaluated on UBIRIS V2 and FRGC datasets and the results show improved performance compared to existing algorithms.
Abstract: The performance of iris recognition reduces when the images are captured at a distance. However, such images generally contain periocular region which can be utilized for person recognition. In this research, we propose a novel context switching algorithm that dynamically selects the best descriptor for color iris and periocular regions. Using predefined protocols, the performance of the proposed algorithm is evaluated on UBIRIS V2 and FRGC datasets, and the results show improved performance compared to existing algorithms.
10 citations
Cites background from "Biometric match score fusion using ..."
...Thereafter, several approaches were proposed to extend the state-of-art in ocular biometrics [3], [5], [6], [8], [10], [16], [18], [19], [24], [25], [26]....
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TL;DR: The proposed iris recognition system is able to handle various challenging issues which may occur during image acquisition in near-infrared and/or visible wavelength lights under constrained and less-constrained environments and demonstrates great perseverance for recognizing subjects in both stable and adverse situations.
Abstract: A novel iris recognition system is proposed in this paper. The proposed system is able to handle various challenging issues which may occur during image acquisition in near-infrared and/or visible wavelength lights under constrained and less-constrained environments. The proposed system demonstrates great perseverance for recognizing subjects in both stable and adverse situations. During recognition, the system performs image preprocessing, feature extraction, and classification tasks. During preprocessing, an annular iris portion is segmented out from an input eyeball image, and for this, two different segmentation approaches: one for near-infrared images and another for visible wavelength images, have been proposed. A novel patch-based histogram-type feature (ensemble of patch statistics) which adopts a statistical approach of texture analysis is employed during feature extraction. For the proposed system, the extensive experimental results have been demonstrated using ten benchmark iris databases, namely MMU1, UPOL, IITD, UBIRIS.v1, CASIA-Interval-v3, CASIA-Iris-Twins, CASIA-Iris-Thousand, CASIA-Iris-Distance, CASIA-Iris-Syn, and UBIRIS.v2. The performance of the proposed system is compared with the state-of-the-art methods on these databases and the comparisons show significant out-performance on the competing methods.
9 citations
01 Dec 2015
TL;DR: A multi-instances finger vein recognition using feature level fusion is proposed and local Hybrid Binary Gradient Contour is proposed as the finger texture descriptor and SVM is used for classification.
Abstract: In a finger vein authentication system, the image of a finger acquired for recognition always suffers from noises due to imperfect acquisition device, signal distortion, and variability of individual physical appearance over time. To improve the system performance, we propose a multi-instances finger vein recognition using feature level fusion. Local Hybrid Binary Gradient Contour (LHBGC) is proposed as the finger texture descriptor and SVM is used for classification. Experiments are conducted using the Shandong finger vein database (SDUMLA-HMT) and also the University Sains Malaysia finger vein database (FV-USM). Experimental results show a significant increase in performance accuracy when more than one fingers are combined, with an EER as low as 0.0038%.
8 citations
19 Aug 2016
TL;DR: This research presents a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more and yields state-of-the-art results.
Abstract: Person recognition is a challenging research problem particularly if the images are captured at a distance and only ocular region is present. In this research, we present a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more. Using these features and random decision forest, fusion and classification is performed and verification results are reported. On CASIA V4-at-a-distance and FOCS databases, the proposed algorithm yields state-of-the-art results; particularly achieving over 61% genuine accept rate at 0.1% false accept rate on complete CASIA V4-at-a-distance database.
7 citations
Cites background from "Biometric match score fusion using ..."
...3 shows sample images from the CASIA and FOCS databases respectively....
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References
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01 Jan 1995TL;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?.
40,147 citations
"Biometric match score fusion using ..." refers methods in this paper
...SVM [15] is a widely used classification technique that avoids over-fitting and leads to good generalization by finding the separating hyperplane that maximizes the margin width....
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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.
5,670 citations
"Biometric match score fusion using ..." refers background in this paper
...Kittler et al. developed a theoretical framework for combining the classifiers which includes product rule, sum rule, max rule, min rule, median rule and majority voting [11]....
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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
5,116 citations
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.
2,829 citations