Confidence measures for multimodal identity verification

Bengio, Samy; Marcel, Christine; Marcel, Sébastien; Mariéthoz, Johnny

doi:10.1016/s1566-2535(02)00089-1

Cited by 64 publications

(41 citation statements)

References 18 publications

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“…These methods are particularly suitable under different settings and are described in this paper in greater detail. The discussion also includes how the fusion process utilizes the feature and decision level correlation among the modalities [103], and how the contextual [100] and the confidence information [18] influences the overall fusion process.…”

Section: Introductionmentioning

confidence: 99%

Multimodal fusion for multimedia analysis: a survey

et al. 2010

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This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

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Section: Introductionmentioning

confidence: 99%

Multimodal fusion for multimedia analysis: a survey

et al. 2010

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“…The concept of confidence measure of matching scores was also studied by [37]. In that work Bengio et al demonstrated that the confidence of matching scores can help in the fusion process.…”

Section: Quality-based Multimodal Biometricsmentioning

confidence: 99%

Multiple classifiers in biometrics. Part 2: Trends and challenges

et al. 2018

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“…For this purpose, a plethora of quality measures have recently been proposed in the literature for various biometric modalities, e.g., fingerprint [23], [24], iris [25], face [26], speech [27], signature [28], and classifier-dependent measure (confidence) [29], [30]. The proposed quality measures, in general, aim to quantify the degree of excellence or conformance of biometric samples to some predefined criteria known to influence the system performance.…”

Section: Literature Surveymentioning

confidence: 99%

Multibiometrics for Identity Authentication: Issues, Benefits and Challenges

Kiltter

Poh

2008

2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems

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Abstract-Multi biometric systems exploit different biometric traits, multiple samples and multiple algorithms to establish the identity of an individual. Over any single biometric system, they have the advantage of increasing the population coverage, offering user choice, making biometric authentication systems more reliable and resilient to spoofing, and most importantly, improving the authentication performance. However, both the design and deployment of multi biometric systems raise many issues. These include system architecture, fusion methodology, selection of component biometric experts based on their accuracy and diversity, measurement of their quality, reliability and competence, as well as overall system usability, and economic viability. These issues will be addressed and possible ways forward discussed.

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Confidence measures for multimodal identity verification

Cited by 64 publications

References 18 publications

Multimodal fusion for multimedia analysis: a survey

Multimodal fusion for multimedia analysis: a survey

Multiple classifiers in biometrics. Part 2: Trends and challenges

Multibiometrics for Identity Authentication: Issues, Benefits and Challenges

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