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Evaluation Methodologies for Biometric Presentation Attack Detection

  • Ivana Chingovska
  • Amir MohammadiEmail author
  • André Anjos
  • Sébastien Marcel
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Presentation attack detection (PAD, also known as anti-spoofing) systems, regardless of the technique, biometric mode or degree of independence of external equipment, are most commonly treated as binary classification systems. The two classes that they differentiate are bona-fide and presentation attack samples. From this perspective, their evaluation is equivalent to the established evaluation standards for the binary classification systems. However, PAD systems are designed to operate in conjunction with recognition systems and as such can affect their performance. From the point of view of a recognition system, the presentation attacks are a separate class that need to be detected and rejected. As the problem of presentation attack detection grows to this pseudo-ternary status, the evaluation methodologies for the recognition systems need to be revised and updated. Consequentially, the database requirements for presentation attack databases become more specific. The focus of this chapter is the task of biometric verification and its scope is three-fold: first, it gives the definition of the presentation attack detection problem from the two perspectives. Second, it states the database requirements for a fair and unbiased evaluation. Finally, it gives an overview of the existing evaluation techniques for presentation attacks detection systems and verification systems under presentation attacks.

Notes

Acknowledgements

The authors would like to thank the projects BEAT (http://www.beat-eu.org) and TABULA RASA (http://www.tabularasa-euproject.org) both funded under the 7th Framework Programme of the European Union (EU) (grant agreement number 284989 and 257289) respectively. The revision of this chapter was supported under the project on Secure Access Control over Wide Area Networks (SWAN) funded by the Research Council of Norway (grant no. IKTPLUSS 248030/O70). and by the Swiss Center for Biometrics Research and Testing.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ivana Chingovska
    • 1
  • Amir Mohammadi
    • 1
    Email author
  • André Anjos
    • 1
  • Sébastien Marcel
    • 1
  1. 1.Idiap Research InstituteMartignySwitzerland

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