2002
DOI: 10.1016/s0031-3203(01)00103-0
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Decision-level fusion in fingerprint verification

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Cited by 321 publications
(46 citation statements)
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References 24 publications
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“…Our approach consists of detecting discrete components in the genuine and impostor matching score distributions, and then modeling the observed distribution of matching scores as a mixture of discrete and continuous components. Hence, this approach generalizes the work of [11,12].…”
Section: Estimation Of Marginal Distributionssupporting
confidence: 56%
See 1 more Smart Citation
“…Our approach consists of detecting discrete components in the genuine and impostor matching score distributions, and then modeling the observed distribution of matching scores as a mixture of discrete and continuous components. Hence, this approach generalizes the work of [11,12].…”
Section: Estimation Of Marginal Distributionssupporting
confidence: 56%
“…Thus, for a set of genuine and impostor matching scores, it is important to be able to estimate f gen (x) and f imp (x) reliably and accurately. Previous studies by Griffin [11] and Prabhakar et al [12] assume that the distribution function F has a continuous density with no discrete components. In reality, most matching algorithms apply thresholds at various stages in the matching process.…”
Section: Estimation Of Marginal Distributionsmentioning
confidence: 99%
“…Recently, Lu Xiaoguang et. al., combined 3 independent classifiers (PCA, ICA, LDA) in their method, and experimental results show that the combination classifier is better than any of the individual classifiers [1][2][3]. Aiming at the problem of face recognition, this paper presents a method combined with support vector machine and the distance metric, constituting two class classifier combination method.…”
Section: Face Recognition Methods Based On Multiple Classifier Combinamentioning
confidence: 99%
“…The steps include segmentation, 2,32 enhancement, 10,28,33 and matching, 8,24 etc. Second, researchers propose to combine the evidence obtained from multiple sources including multiple biometric traits, 1,9 multiple sensors, 18 multiple representations and matchers, 13,20,21 multiple¯ngers, 21 and multiple impressions of a same¯nger. 14,23,26 Third, new features are explored for matching beyond the most commonly used minutiae feature.…”
Section: Introductionmentioning
confidence: 99%