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.read more
Citations
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Book ChapterDOI
Noisy Iris Verification: A Modified Version of Local Intensity Variation Method
TL;DR: A modified version of local intensity variation method is proposed to enhance the efficiency of identification system while dealing with degradation factors presented in iris texture and results on a private database show that verification performance remains acceptable while the original method suffers from a dramatic degradation.
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On Linear Combinations of Dichotomizers for Maximizing the Area Under the ROC Curve
TL;DR: A method for the linear combination of several dichotomizers aimed at maximizing the area under the receiver operating characteristic (ROC) curve of the resulting classification system for real applications where it is difficult to exactly determine the key parameters such as costs and priors.
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Sample partition and grouped sparse representation
TL;DR: Experimental results on four databases show the proposed scheme can improve the recognition rate in image-based recognition.
Journal ArticleDOI
Complex Numbers as a Compact Way to Represent Scores and Their Reliability in Recognition by Multi-Biometric Fusion
TL;DR: A unified representation of the recognition score and of the corresponding quality/reliability value into a single complex number provides simplification and speed up of fusion of multi-classifier results.
Proceedings ArticleDOI
Image disparity in cross-spectral face recognition: mitigating camera and atmospheric effects
TL;DR: This work considers a scenario where visible light images are acquired at a short standoff distance while IR images are long range data, and proposes two approaches that allow to coordinate image quality of visible and IR face images.
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.