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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Journal ArticleDOI
Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion
TL;DR: This work uses ACO as an optimization technique to select the fusion parameter like weights for the different biometric matcher and fusion rule which is used further for score level fusion and shows that the multibiometric system using ACO based on sum rule is outperform than the other fusion rule like product, tanh and exponential sum.
Dissertation
Application-driven Advances in Multi-biometric Fusion
TL;DR: A novel performance anchored score normalization technique is presented that aligns certain performance-related score values in the fused biometric sources leading to more accurate multi-biometric decisions when compared to conventional normalization approaches.
Journal ArticleDOI
Inverse transformation based weighted fusion for face recognition
TL;DR: An algorithm that can integrate original images and virtual images and outperforms other state-of-the-art methods in recognition accuracy and has a high computational efficiency is proposed.
Proceedings ArticleDOI
Improving classifier fusion via Pool Adjacent Violators normalization
TL;DR: This research explores an alternative method to combine classifiers at the score level and proposes the PAV algorithm for classifier fusion on publicly available NIST multi-modal biometrics score dataset, finding that it provides several advantages over existing techniques and is able to further improve the results obtained by other approaches.
Journal ArticleDOI
Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition.
TL;DR: Li et al. as discussed by the authors proposed a cascaded face enhancement that combines an existing denoising algorithm (BM3D) with a new deep-learning-based deblurring model (named SVDFace).
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.