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Journal ArticleDOI

Likelihood Ratio-Based Biometric Score Fusion

TLDR
Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
Abstract
Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.

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Citations
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Journal ArticleDOI

Deep binary codes for large scale image retrieval

TL;DR: A novel and effective method to create compact binary codes (deep binary codes) based on deep convolutional features for image retrieval based on a generic model which does not require additional training for new image domains and the dynamic late fusion scheme is query adaptive.
Journal ArticleDOI

QFuse: Online learning framework for adaptive biometric system

TL;DR: This paper presents an adaptive context switching algorithm coupled with online learning to address the scalability and accommodate the variations in data distribution of biometrics.
Journal ArticleDOI

A confidence-based late fusion framework for audio-visual biometric identification

TL;DR: This paper presents a confidence-based late fusion framework and its application to audio-visual biometric identification and proposes modifications to the highest rank and Borda count rank fusion rules to incorporate the matcher confidence.
Patent

Distributing biometric authentication between devices in an ad hoc network

TL;DR: In this article, a biometric authentication of a user between multiple devices in an ad hoc personal wireless network is discussed, where each secondary device performs additional authentication of the same user using a low reliability biometric sensor such as a digital camera for facial recognition, a microphone for voice recognition or an accelerometer for gesture recognition.
Journal ArticleDOI

Incremental granular relevance vector machine

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.
References
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BookDOI

Density estimation for statistics and data analysis

TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Book

Testing statistical hypotheses

TL;DR: The general decision problem, the Probability Background, Uniformly Most Powerful Tests, Unbiasedness, Theory and First Applications, and UNbiasedness: Applications to Normal Distributions, Invariance, Linear Hypotheses as discussed by the authors.
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.
Journal ArticleDOI

Unsupervised learning of finite mixture models

TL;DR: The novelty of the approach is that it does not use a model selection criterion to choose one among a set of preestimated candidate models; instead, it seamlessly integrate estimation and model selection in a single algorithm.
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.
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