Expert conciliation for multi modal person authentication systems by Bayesian statistics

Bigün, Elizabeth Saers; Bigün, Josef; Duc, Benoît; Fischer, Stefan

doi:10.1007/bfb0016008

Cited by 126 publications

(106 citation statements)

References 10 publications

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“…This basic fusion method consists of averaging the matching scores provided by the different matchers. Under some mild statistical assumptions [20,21] and with the proper matching score normalization [22], this simple method is demonstrated to give good results for the biometric authentication problem. This fact is corroborated in a number of studies [21,23].…”

Section: Quality-based Score Fusionmentioning

confidence: 99%

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Fiérrez-Aguilar

Chen

Ortega‐Garcia

et al. 2005

Advances in Biometrics

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.

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Section: Quality-based Score Fusionmentioning

confidence: 99%

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Fiérrez-Aguilar

Chen

Ortega‐Garcia

et al. 2005

Advances in Biometrics

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“…In contrast, multimodal biometric systems combine information from its component modalities to arrive at a decision [3]. Several studies [4][5][6][7][8] have demonstrated that by consolidating information from multiple sources, better performance can be achieved compared to the individual unimodal systems. In a multimodal biometric system, integration can be done at (i) feature level, (ii) matching score level, or (iii) decision level.…”

Section: Introductionmentioning

confidence: 99%

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

Dass

Nandakumar

Jain

2005

Lecture Notes in Computer Science

136

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Abstract. A multimodal biometric system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. We propose an optimal framework for combining the matching scores from multiple modalities using the likelihood ratio statistic computed using the generalized densities estimated from the genuine and impostor matching scores. The motivation for using generalized densities is that some parts of the score distributions can be discrete in nature; thus, estimating the distribution using continuous densities may be inappropriate. We present two approaches for combining evidence based on generalized densities: (i) the product rule, which assumes independence between the individual modalities, and (ii) copula models, which consider the dependence between the matching scores of multiple modalities. Experiments on the MSU and NIST multimodal databases show that both fusion rules achieve consistently high performance without adjusting for optimal weights for fusion and score normalization on a case-by-case basis.

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“…Examples are global-learning-global-decision (GG) (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000), local-learning-globaldecision (LG) (Jain and Ross, 2002;Kumar and Zhang, 2003;Indovina et al, 2003;Fierrez-Aguilar et al, 2004;Wang et al, 2004;Toh et al, 2004;Poh and Bengio, 2005), and similarly global-learninglocal-decision (GL) (Jain and Ross, 2002;Toh et al, 2004), and local-learninglocal-decision (LL) (Toh et al, 2004). In the present work we adhere to this taxonomy and extend it by incorporating new items: adapted-learning and adapted-decisions.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…A common practice in most of the reported works on multimodal biometrics is to combine the matching scores obtained from the unimodal systems by using simple rules (e.g., sum, product), statistical methods, or machine learning procedures (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000). A remarkable characteristic of this approach, as compared to the feature-level combination techniques, is the possibility of designing structured multimodal systems by using existing unimodal recognition strategies (Maltoni et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Adapted user-dependent multimodal biometric authentication exploiting general information

Fiérrez-Aguilar

Garcia‐Romero

Ortega‐Garcia

et al. 2005

Pattern Recognition Letters

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This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 26.16 (2005): 2628 – 2639, DOI: 10.1016/j.patrec.2005.06.008A novel adapted strategy for combining general and user-dependent knowledge at the decision-level in multimodal biometric authentication is presented. User- independent, user-dependent, and adapted fusion and decision schemes are com- pared by using a bimodal system based on ¯ngerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this pa- per. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.This work has been supported by the Spanish Ministry for Science and Tech- nology under projects TIC2003-09068-C02-01 and TIC2003-08382-C05-01

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Expert conciliation for multi modal person authentication systems by Bayesian statistics

Cited by 126 publications

References 10 publications

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

Adapted user-dependent multimodal biometric authentication exploiting general information

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