Iris biometric indexing

Iris biometric indexing

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Iris and Periocular Biometric Recognition — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Indexing/retrieving sets of iris biometric signatures has been a topic of increasing popularity, mostly due to the deployment of iris recognition systems in nationwide scale scenarios. In these conditions, for each identification attempt, there might exist hundreds of millions of enrolled identities and is unrealistic to match the probe against all gallery elements in a reasonable amount of time. Hence, the idea of indexing/retrieval is - upon receiving one sample - to find in a quick way a subset of elements in the database that most probably contains the identity of interest, i.e., the one corresponding to the probe. Most of the state-of-the-art strategies to index iris biometric signatures were devised to decision environments with a clear separation between genuine and impostor matching scores. However, if iris recognition systems work in low quality data, the resulting decision environments are poorly separable, with a significant overlap between the distributions of both matching scores. This chapter summarizes the state-of-the-art in terms of iris biometric indexing/retrieval and focuses in an indexing/retrieval method for such low quality data and operates at the code level, i.e., after the signature encoding process. Gallery codes are decomposed at multiple scales, and using the most reliable components of each scale, their position in a n-ary tree is determined. During retrieval, the probe is decomposed similarly, and the distances to multi-scale centroids are used to penalize paths in the tree. At the end, only a subset of branches is traversed up to the last level.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 State of the art
  • 5.3 Indexing/retrieving poorly separated data
  • 5.3.1 Indexing
  • Codes decomposition/reconstruction
  • Determining the number of branches per node
  • 5.3.2 Retrieval
  • 5.3.3 Time complexity
  • 5.4 Performance comparison
  • 5.4.1 Synthetic IrisCodes
  • 5.4.2 Well separated near infra-red data
  • 5.4.3 Poorly separated visible wavelength data
  • 5.5 Conclusions
  • Acknowledgment
  • References

Inspec keywords: iris recognition; database indexing; image coding; image retrieval; image matching; tree data structures; visual databases

Other keywords: iris recognition systems; genuine matching scores; iris biometric signature retrieval; signature encoding process; multiscale centroids; gallery codes; impostor matching scores; n-ary tree; Iris biometric signature indexing; code level

Subjects: Spatial and pictorial databases; Information retrieval techniques; Computer vision and image processing techniques; File organisation; Image and video coding; Image recognition

Preview this chapter:
Zoom in

Iris biometric indexing, Page 1 of 2

| /docserver/preview/fulltext/books/sc/pbse005e/PBSE005E_ch5-1.gif /docserver/preview/fulltext/books/sc/pbse005e/PBSE005E_ch5-2.gif

Related content

This is a required field
Please enter a valid email address