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Usefulness of Existing Iris Databases and Future Priorities George W. Quinn NIST gw@nist.gov Jan 26, 2015 Current State of Iris Recognition Iris recognition is extremely accurate when the quality of the images is good. The IREX III

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George W. Quinn NIST gw@nist.gov Jan 26, 2015

Usefulness of Existing Iris Databases and Future Priorities

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  • Iris recognition is extremely accurate when the quality of the images is good.
  • The IREX III supplemental report found that of 1,013 searches that failed to

return the correct mate (for any of the top 3 matching algorithms), in every case there was some problem with one of the images.

  • Performance of an operational system will be determined by the poorest

quality samples and how frequently they occur.

Current State of Iris Recognition

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Available Iris Datasets

Notre Dame

  • ND-IRIS-0405
  • Cross Sensor
  • Time Lapse
  • Template Aging
  • Contact Lenses
  • Gender Prediction
  • Face / Ocular Challenge

CASIA

  • Iris-Thousand
  • Iris-Interval
  • Twins
  • Long Range
  • Synthetic Iris
  • Iris-Lamp

University of Beira

  • UBIRIS.v1
  • UBIRIS.v1

Clarkson University

  • Q-FIRE (face + iris)
  • Liveness Detection

West Virginia University

  • Multi-modal
  • Off-Axis
  • Synthetic Iris

UTIRIS

  • Visible + Near-IR

BioSecure

  • Desktop Dataset

SmartSensors

  • IrisBase

MultiMedia University

  • MMU1
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Pupil Dilation

“[W]hen matching … images of the same person, larger differences in pupil dilation yield higher template dissimilarities.”

  • Hollingsworth, Bowyer, Flynn

Computer Vision and Image Understanding, 2009 Constricted Dilated

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Pupil Dilation

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Contact Lenses

Two types: 1) Vision correction lenses 2) Patterned contact lenses Vision Correction Patterned Contact spoofing?

Notre Dame has a dataset of iris images of people wearing contact lenses: http://www3.nd.edu/~cvrl/CVRL/Data_Sets.html

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Iris Ageing

Iris Ageing Irreversible changes to the healthy iris or neighboring anatomy that yield mated dissimilarity scores that increase monotonically with time-separation

  • f the compared images.
  • IREX VI
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Iris Ageing*

Figure Source: “Analysis of Template Aging in Iris Biometrics.” Fenker and Bowyer, IEEE Computer Sciety Biometrics Workshop, 2012 * Fenker and Bowyer use a different definition of ‘template ageing’.

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  • IREX VI includes a comprehensive re-analysis of the Notre Dame iris collections.
  • It also searched for an ageing effect in two other iris datasets:
  • NEXUS (Canadian border crossing)
  • OPS (Operational data from DoD)
  • Conclusion: “[W]e find no evidence of a widespread iris ageing effect”
  • The re-analysis of the Notre Dame data concluded that when you normalize for

pupil dilation and eyelid occlusion, the apparent ageing effect goes away.

Decision Threshold Decision Threshold Reject Rate

Iris Ageing

Without normalization With normalization

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Irregular Pupils

The IREX III Failure Analysis Report determined that 45 of 1,013 failed searches probably failed due to abnormal pupil shapes.

Image Sources: medicalpicturesinfo.com, pbs.org

Coloboma Tadpole Pupil

Possible Medical Explanations:

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Illumination

Differences in illumination can make non-flat surfaces appear different.

source: landsat.gsfc.nasa.gov

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Illumination

  • Most cameras illuminate from the front to restrict specular highlights

within the pupil.

  • Differences in illumination can still occur due to ambient lighting.
  • Ambient lighting can also introduce Purkinji Images.
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Illumination

NOTE: CASIA-Iris-Lamp 4.0 contains images where a lamp was turned on near the subject to ‘introduce more intra-class variations’, but the images do not contain noticeable Purkinji Images.

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Eye Colour

  • Does the colour of the eye affect recognition accuracy?
  • Lighter coloured eyes seem to have more pronounced features

(at least according to my own personal observation!) Green Eye Brown Eye

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Eye Colour

FPIR (Enrolled Population Size = 1,600,000) FNIR

0.01 0.02 0.05 0.1 0.0001 0.0002 0.0005 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2

D02P , blue/green/grey D02P , brown G01P , blue/green/grey G01P , brown I02P , blue/green/grey I02P , brown

Light eyes Dark eyes

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Iris as a Forensic

Source: National Geographic Source: 2001: A Space Odyssey (film)

Forensic Science “The application of scientific knowledge and methodology to legal problems and criminal investigations.”

  • legal-dictionary.thefreedictionary.com

Webcam Image

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Future Areas of Research

  • Surgical alterations
  • Neo-natal
  • Ageing
  • Iris at a distance
  • Abnormal Pupils
  • Purkinji Images

Also, larger sets of iris images wouldn’t hurt (i.e. with more subjects represented).

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Datasets Referenced

University of Tehran (UTIRIS) – Visible and Near-IR iris captures. https://utiris.wordpress.com/ Notre Dame (ND) – Iris Ageing, Contact Lenses (and more) http://www3.nd.edu/~cvrl/CVRL/Data_Sets.html CASIA-Iris-Lamp – Images with a side lamp turned on/off http://biometrics.idealtest.org/

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Thanks

George W. Quinn http://iris.nist.gov/