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Addressing the presentation attacks using periocular region for smartphone biometrics

Addressing the presentation attacks using periocular region for smartphone biometrics

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In this chapter, we have explored presentation attacks in ocular biometric system on smartphones in the visible spectrum. We have discussed both kind of attacks - print attacks and electronic screen attacks. We have employed two publicly available databases that correspond to large-scale image-based artefact, MobiLive 2014 dataset, and for video-based artefacts, PAVID dataset.

Chapter Contents:

  • 13.1 Introduction
  • 13.2 Database
  • 13.2.1 MobILive 2014 Database
  • 13.2.2 PAVID Database
  • 13.2.2.1 PAVID - Bona Fide iris video database
  • 13.2.2.2 PAVID - attack iris video database
  • 13.3 Vulnerabilities towards presentation attacks
  • 13.3.1 Vulnerability analysis using the PAVID
  • 13.4 PAD techniques
  • 13.4.1 Metrics for PAD algorithms
  • 13.4.2 Texture features for PAD
  • 13.4.2.1 Image quality features
  • 13.4.2.2 Texture features from LBP
  • 13.4.2.3 Texture features from binarized statistical image features
  • 13.4.2.4 Laplacian pyramid frequency response features
  • 13.5 Experiments and results
  • 13.5.1 Results on MobiLive 2014 database
  • 13.5.2 Results on the PAVID database
  • 13.6 Discussions and conclusion
  • Acknowledgments
  • References

Inspec keywords: iris recognition; video signal processing; smart phones

Other keywords: electronic screen attacks; presentation attacks; image-based artefact; smartphones; print attacks; MobiLive 2014 dataset; PAVID dataset; video-based artefacts; periocular region

Subjects: Ubiquitous and pervasive computing; Image recognition; Video signal processing; Computer vision and image processing techniques

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