Automated Iris Recognition Technology & Iris Biometric System
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Automated Iris Recognition Technology & Iris Biometric System
The Iris as a Biometrics: The iris is an overt body that is available for remote assessment with the aid of a machine vision system to do automated iris recognition.
Iris recognition technology combines computer vision, pattern recognition, statistical inference, and optics.
The spatial patterns that are apparent in the human iris are highly distinctive to an individual.
Clinical observations
Developmental biology
Overview
Its suitability as an exceptionally accurate biometric derives from its
extremely data-rich physical structure
genetic independence — no two eyes are the same
patterns apparently stable throughout life
physical protection by a transparent window (the cornea), highly protected by internal organ of the eye
externally visible, so noninvasive — patterns imaged from a distance
Overview
The disadvantages to use iris as a biometric measurement are
Small target (1 cm) to acquire from a distance (about 1 m)
Moving target
Located behind a curved, wet, reflecting surface
Obscured by eyelashes, lenses, reflections
Partially occluded by eyelids, often drooping
Deforms non-elastically as pupil changes size
Illumination should not be visible or bright
PART I: Iris Recognition: An Emerging Biometric Technology
R. Wildes, "Iris Recognition: An Emerging Biometric Technology", Proceedings of the IEEE, vol 85, no. 9, pp. 1348-1363, 1997.
Outline
Technical Issues **
Image Acquisition
Iris Localization
Pattern Matching
Systems and Performance
** (Throughout the discussion in this paper, the iris-recognition systems of Daugman and Wildes et al. will be used to provide illustrations.)
Technical Issues
I. Image Acquisition
Why important?
One of the major challenges of automated iris recognition is to capture a high-quality image of the iris while remaining noninvasive to the human operator.
Concerns on the image acquisition rigs
Obtained images with sufficient resolution and sharpness
Good contrast in the interior iris pattern with proper illumination
Well centered without unduly constraining the operator
Artifacts eliminated as much as possible
I. Image Acquisition - Rigs
The Daugman image-acquisition rig
I. Image Acquisition - Rigs
The Wildes et al. image-acquisition rig
I. Image Acquisition - Results
Discussion
In common:

Easy for a human operator to master
Use video rate capture
Difference:
Illumination
The Daugman’s system makes use of an LED-based point light source in conjunction with a standard video camera.
The Wildes et al. system makes use of a diffuse source and polarization in conjunction with a low-light level camera.
Operator self-position
The Daugman’s system provides the operator with live video feedback
The Wildes et al. system provides a reticle to aid the operator in positioning
II. Iris Localization
Purpose: to localize that portion of the acquired image that corresponds to an iris
In particular, it is necessary to localize that portion of the image derived from inside the limbus (the border between the sclera and the iris) and outside the pupil.
Desired characteristics of iris localization:
Sensitive to a wide range of edge contrast
Robust to irregular borders
Capable of dealing with variable occlusions
II. Iris Localization
The Daugman system fits the circular contours via gradient ascent on the parameters so as to maximize
II. Iris Localization
The Wildes et al. system performs its contour fitting in two steps. (histogram-based approach)
First, the image intensity information is converted into a binary edge-map
where
and
Second, the edge points vote to instantiate particular contour parameter values.
II. Iris Localization
The voting procedure of the Wildes et al. system is realized via Hough transforms on parametric definitions of the iris boundary contours.
Illustrative Results of Iris Localization
III. Pattern Matching
Four steps:

1) bringing the newly acquired iris pattern into spatial alignment with a candidate data base entry;
2) choosing a representation of the aligned iris patterns that makes their distinctive patterns apparent;
3) evaluating the goodness of match between the newly acquired and data base representations;
4) deciding if the newly acquired data and the data base entry were derived from the same iris based on the goodness of match.
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