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Robust iris image segmentation

Robust iris image segmentation

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In this chapter we presented current trends in iris segmentation, summarising the advances in state-of-the-art individual segmentation, shedding light on the pitfalls of NIR vs. VIS iris segmentation and illustrating means to tune existing iris segmentation towards specific datasets. We highlighted different forms of segmentation accuracy assessment and evaluated different approaches to combine segmentation algorithms. To get more stable results we integrated segmentation quality prediction in the fusion process.Tests on ground-truth confirm this positive effect across the board. For the goal of developing more robust iris segmentation techniques, multi-segmentation fusion and quality prediction can be a versatile tool to obtain more stable and more accurate results. However, performing fusion and running multiple segmentation algorithms impacts the processing time. This was not explicitly covered and further research on frame throughput might be necessary for video-based iris segmentation.

Chapter Contents:

  • 3.1 Introduction
  • 3.1.1 Segmentation accuracy
  • 3.1.2 Iris segmentation quality
  • 3.2 Advances in iris segmentation
  • 3.2.1 From circular models to active contours
  • 3.2.2 Near infrared vs. visible range segmentation
  • 3.2.3 Learning-based techniques
  • 3.2.4 Segmentation fusion
  • 3.3 Experiments
  • 3.3.1 Individual NIR vs. VIS performance
  • 3.3.2 Impact of tuning
  • 3.3.3 Combinations of segmentation performance
  • 3.4 Conclusion and future work
  • References

Inspec keywords: video signal processing; iris recognition; image segmentation

Other keywords: VIS iris segmentation; segmentation quality prediction; NIR iris segmentation; robust video iris image segmentation

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

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