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Iris image quality metrics with veto power and nonlinear importance tailoring

Iris image quality metrics with veto power and nonlinear importance tailoring

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Linear combinations of metrics for assessing biometric sample quality are weak, because they lack veto power. For example, a good score for a sharp focus of an ocular image would `compensate' in an additive combination for the fact that the eyelids are fully closed; or fully open eyelids would compensate for the image being many diopters out-of-focus. Normalised multiplicative quality factors are better because they are punitive, and thereby confer veto powers. This chapter explains the basis for the product of power functions which underlie the ISO/IEC 29794-6 Iris Image Sample Quality Standard, in particular how the exponents of the power functions allow importance tailoring of each element.

Chapter Contents:

  • 4.1 Introduction
  • 4.1.1 Related work
  • 4.2 Normalisation of individual quality measures
  • 4.3 Effectiveness of multiplying quality factors
  • 4.4 Importance tailoring
  • 4.5 Error reject curves
  • 4.6 Discipline in punishment
  • 4.7 Predictive value of the quality pseudonorm
  • 4.8 Testing and adoption into an ISO/IEC quality standard
  • 4.9 A possible criticism of the method
  • Acknowledgement
  • References

Inspec keywords: iris recognition

Other keywords: iris image quality metrics; ocular image; veto power; sharp focus; nonlinear importance tailoring; biometric sample quality assessment; power functions; fully open eyelids; ISO/IEC 29794-6 Iris Image Sample Quality Standard; normalised multiplicative quality factors; fully closed eyelids

Subjects: Image recognition; Computer vision and image processing techniques

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