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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Fusion Framework for Multimodal Biometric Person Authentication System
TL;DR: This study investigates the need for multiple sensors, multiple recognition algorithms and multiple fusion levels and their efficiency for a Person Authentication System (PAS) with face, fingerprint and iris biometrics using Joint Directors of Laboratories (JDL) fusion model.
Proceedings ArticleDOI
Adaptive biometric authentication using nonlinear mappings on quality measures and verification scores
TL;DR: Three methods to improve the performance of biometric matchers based on vectors of quality measures associated with biometric samples are described, which are suitable for any biometric modality and show significant performance improvements when applied to iris biometrics.
Proceedings ArticleDOI
Biometric match score fusion using RVM: A case study in multi-unit iris recognition
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.
Book ChapterDOI
Multimodal Information Fusion
Norman Poh,Josef Kittler +1 more
TL;DR: This chapter gives an overview of multimodal information fusion from the machine-learning perspective and examples of HCI applications include audio-visual speech recognition, gesture recognition, emotional recognition, and person recognition using biometrics.
Journal ArticleDOI
Optimum scheme selection for face–iris biometric
Maryam Eskandari,Omid Sharifi +1 more
TL;DR: Experimental results on verification rates demonstrate a significant improvement of proposed combined level fusion scheme over unimodal and multimodal fusion methods.
References
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Journal ArticleDOI
Score normalization in multimodal biometric systems
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