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Contact lens detection in iris images

Contact lens detection in iris images

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Iris and Periocular Biometric Recognition — Recommend this title to your library

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Iris texture provides the means for extremely accurate uni-modal person identification. However, the accuracy of iris-based biometric systems is sensitive to the presence of contact lenses in acquired sample images. This is especially true in the case of textured (cosmetic) contact lenses that can be effectively used to obscure the original iris texture of a subject and consequently to perform presentation attacks. Since also transparent contact lenses can degrade matching rates, automatic detection and classification of different contact lens types is needed in order to improve the robustness of iris-based biometric systems. This chapter introduces the problem of contact lens detection with particular focus on cosmetic contact lenses. The state of the art is analysed thoroughly and a case study on generalised textured contact lens detection is provided. The potential future research directions are also discussed.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 Literature review on contact lens detection
  • 12.2.1 Textured contact lens detection
  • 12.2.2 Classification of contact lens type
  • 12.3 Case study: generalised software-based textured contact lens detection
  • 12.3.1 Pre-processing
  • 12.3.2 Texture description
  • 12.3.3 Experimental analysis
  • 12.3.3.1 Experimental set-up
  • 12.3.3.2 Leave-one-out lens validation
  • 12.3.3.3 Device independent validation
  • 12.4 Discussion
  • 12.4.1 Further work on generalised textured contact lens detection
  • 12.4.2 The role of pre-processing in contact lens detection
  • 12.4.3 On the evaluation of contact lens detection algorithms
  • 12.5 Conclusions
  • Acknowledgement
  • References

Inspec keywords: image texture; iris recognition

Other keywords: iris-based biometric systems; generalised textured contact lens detection; cosmetic contact lenses

Subjects: Computer vision and image processing techniques; Image recognition

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