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Multimodal Anti-spoofing in Biometric Recognition Systems

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Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

While multimodal biometric systems were commonly believed to be intrinsically more robust to spoof attacks than unimodal systems, recent results provided clear evidence that they can be evaded by spoofing a single biometric trait. This pointed out that also multimodal systems require specific anti-spoofing measures. In this chapter, we introduce the issue of multimodal anti-spoofing, and give an overview of state-of-the-art anti-spoofing measures. Such measures mainly consist of developing ad hoc score fusion rules that are based on assumptions about the match score distribution produced by fake biometric traits. We discuss the pros and cons of existing measures, and point out the current challenges in multimodal anti-spoofing.

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Notes

  1. 1.

    Also the LR rule and similar ones can be referred to as trained rules, as they require the estimation of likelihoods.

  2. 2.

    In [9] quality measures of the biometric samples in each modality were also considered. For the sake of simplicity we do not include them in our description.

  3. 3.

    The clients of a chimerical dataset are usually referred to as “virtual” clients, since they do not correspond to a real person or identity. They are indeed created by randomly associating the biometric traits of different “real” clients.

  4. 4.

    http://www.nist.gov/itl/iad/ig/nbis.cfm

  5. 5.

    http://www.neurotechnology.com/verifinger.html

  6. 6.

    http://www.cs.colostate.edu/evalfacerec/algorithms5.php

  7. 7.

    http://www.tabularasa-euproject.org/

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Acknowledgments

This work has been partly supported by the TABULA RASA project, 7th Framework Research Programme of the European Union (EU), grant agreement number: 257289; by the project CRP-18293 funded by Regione Autonoma della Sardegna (RAS), L.R. 7/2007, Bando 2009; and by a grant awarded to B. Biggio by RAS, PO Sardegna FSE 2007-2013, L.R. 7/2007 “Promotion of the scientific research and technological innovation in Sardinia.”

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Giorgio Fumera, Gian Luca Marcialis, Battista Biggio & Fabio Roli

  2. Department of Electrical and Computer Engineering, Clarkson University, PO Box 5720, Potsdam, NY, 13699, USA

    Stephanie Caswell Schuckers

Authors
  1. Giorgio Fumera
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  2. Gian Luca Marcialis
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  3. Battista Biggio
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  4. Fabio Roli
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  5. Stephanie Caswell Schuckers
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Correspondence to Giorgio Fumera .

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Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, United Kingdom

    Mark S. Nixon

  3. Institute of Automation Center for Biometrics and Security Resea, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

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Fumera, G., Marcialis, G.L., Biggio, B., Roli, F., Schuckers, S.C. (2014). Multimodal Anti-spoofing in Biometric Recognition Systems. In: Marcel, S., Nixon, M., Li, S. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_9

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  • DOI: https://doi.org/10.1007/978-1-4471-6524-8_9

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