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The Rise of Data-Driven Models in Presentation Attack Detection

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Book cover Deep Biometrics

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Biometric systems are prevalent in access control but are vulnerable to frauds. A typical attempt of violating them is through presentation attacks, in which synthetic data is directly presented to an acquisition sensor to deceive these systems. A well-designed biometric system should have a presentation attack detection (PAD) module. A fruitful way to perform PAD is to model properties of peculiar traits (artifacts) in synthetic data. Studies have been advocating for approaches that seek to model the artifacts automatically from data (data-driven), achieving state-of-the-art results in PAD. However, the following questions arise from this literature: Which approaches are the state of the art? When do these approaches fail? How can such approaches complement the proposed ones based on human knowledge on PAD? How robust are these approaches under cross-dataset scenarios? Are these approaches robust against new attack types (e.g., face morphing)? Do these methods provide other ways to perform PAD, for example, using open-set classifiers rather than the classical binary formulation? Are these methods applicable to the multi-biometric setting? In this chapter, we address these questions through a literature review, focusing on three biometric modalities: face, fingerprint, and iris.

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Notes

  1. 1.

    BBC News: https://www.bbc.com/news/world-latin-america-21756709.

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Acknowledgements

The authors thank the financial support of the European Union through the Horizon 2020 Identity Project as well as the São Paulo Research Foundation—Fapesp, through the grant #2017/12646-3 (DéjàVu), and the Brazilian Coordination for the Improvement of Higher Education Personnel—Capes, through the DeepEyes grant.

Author information

Authors and Affiliations

  1. Reasoning for Complex Data Laboratory, Institute of Computing, University of Campinas, Campinas, SP, Brazil

    Luis A. M. Pereira, Allan Pinto, Fernanda A. Andaló, Alexandre M. Ferreira, Bahram Lavi, Aurea Soriano-Vargas, Marcos V. M. Cirne & Anderson Rocha

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  1. Luis A. M. Pereira
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  2. Allan Pinto
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  3. Fernanda A. Andaló
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  4. Alexandre M. Ferreira
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  6. Aurea Soriano-Vargas
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  7. Marcos V. M. Cirne
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  8. Anderson Rocha
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Corresponding author

Correspondence to Anderson Rocha .

Editor information

Editors and Affiliations

  1. School of Computing and Communication, InfoLab21, Lancaster University, Lancaster, UK

    Richard Jiang

  2. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

    Chang-Tsun Li

  3. Electrical Engineering and Computer Science, School of Electronics, Queen’s University Belfast, Belfast, UK

    Danny Crookes

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Weizhi Meng

  5. GREYC research Lab, ENSICAEN, Caen, France

    Christophe Rosenberger

Appendices

Appendix 1: Datasets and Research Work

See Table 4.

Appendix 2: List of Acronyms

BSIF:

Binary statistical image features

CNN:

Convolutional neural network

DBM:

Deep Boltzmann machine

DCNN:

Deep convolutional neural network

DNN:

Deep neural network

HTER:

Half total error rate

LBP:

Local binary pattern

MLP:

Multilayer perceptron

PA:

Presentation attack

PAD:

Presentation attack detection

SAE:

Sparse auto-encoder

SVM:

Support vector machines

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Pereira, L.A.M. et al. (2020). The Rise of Data-Driven Models in Presentation Attack Detection. In: Jiang, R., Li, CT., Crookes, D., Meng, W., Rosenberger, C. (eds) Deep Biometrics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-32583-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-32583-1_13

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  • Online ISBN: 978-3-030-32583-1

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