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
Optimal Face-Iris Multimodal Fusion Scheme
Omid Sharifi,Maryam Eskandari +1 more
TL;DR: This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities and demonstrates a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.
Journal ArticleDOI
Biometric Recognition of Infants using Fingerprint, Iris, and Ear Biometrics
Yaseen Moolla,Anton de Kock,Gugulethu Mabuza-Hocquet,Cynthia Sthembile Ntshangase,Norman Nelufule,Portia Khanyile +5 more
TL;DR: In this article, the authors collected fingerprint, iris, and outer ear shape biometric information from infants and compared the advantages and disadvantages of using each modality during the first year of life.
Journal ArticleDOI
Towards a multi-source fusion approach for eye movement-driven recognition
TL;DR: A two-stage fusion approach with the employment of stimulus-specific and algorithm-specific weights for fusing the information from different matchers based on their identification efficacy is proposed, which shows a considerable improvement in biometric recognition accuracy.
Proceedings ArticleDOI
Multimodal biometric systems
TL;DR: By combining multiple sources of information, these systems improve matching performance, increase population coverage, deter spoofing, and facilitate indexing in multimodal biometric systems.
Journal ArticleDOI
Combining biometric matchers by means of machine learning and statistical approaches
TL;DR: This work confirms that the fusion of different state-of-the-art fingerprint matchers can lead to a significant performance gain with respect to a single matcher.
References
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