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Unification of Evidence Theoretic Fusion Algorithms: A Case Study in Level-2 and Level-3 Fingerprint Features
TLDR
This paper formulates an evidence-theoretic multimodal unification approach using belief functions that take into account the variability in biometric image characteristics that is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.Abstract:
This paper formulates an evidence-theoretic multimodal unification approach using belief functions that takes into account the variability in biometric image characteristics. While processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions.read more
Citations
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Journal ArticleDOI
A New Human Identification Method: Sclera Recognition
TL;DR: The experimental results show that sclera recognition is a promising new biometrics for positive human ID.
Journal ArticleDOI
Ocular biometrics
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.
Journal ArticleDOI
Estimating and Fusing Quality Factors for Iris Biometric Images
TL;DR: This paper designs a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, off-angle, occlusion, lighting, specular reflection, and pixel counts and fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning.
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Overview of the combination of biometric matchers
Alessandra Lumini,Loris Nanni +1 more
TL;DR: Several systems and architectures related to the combination of biometric systems, both unimodal and multimodal, are overviews, classifying them according to a given taxonomy, and a case study for the experimental evaluation of methods for biometric fusion at score level is presented.
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Multiple classifiers in biometrics. Part 2: Trends and challenges
TL;DR: Recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way are presented and methods are described in a general way so they can be applied to other information fusion problems as well.
References
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Journal ArticleDOI
On combining classifiers
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.
Proceedings Article
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
TL;DR: This presentation reports results of applying the Support Vector method to problems of estimating regressions, constructing multidimensional splines, and solving linear operator equations.
Journal ArticleDOI
Score normalization in multimodal biometric systems
TL;DR: Study of the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user found that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods.
Journal ArticleDOI
On-line fingerprint verification
TL;DR: An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner and an alignment-based elastic matching algorithm has been developed.
Book
Handbook of Multibiometrics
TL;DR: Details multi-modal biometrics and its exceptional utility for increasingly reliable human recognition systems and the substantial advantages of multimodal systems over conventional identification methods.