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Finding An Iris In An Image

Example of an iris pattern, imaged monochromatically at a distance of about 35 cm. The outline overlay shows results of the iris and pupil localization and eyelid detection steps. The bit stream in the top left is the result of demodulation with complex-valued two dimensional (2-D) Gabor wavelets to encode the phase sequence of the iris pattern.

To capture the rich details of iris patterns, an imaging system should resolve a minimum of 70 pixels in iris radius. In the field trials to date, a resolved iris radius of 80–130 pixels has been more typical. Monochrome CCD cameras (480 640) have been used because NIR illumination in the 700–900-nm band was required for imaging to be unintrusive to humans. Some imaging platforms deployed a wide-angle camera for coarse localization of eyes in faces, to steer the optics of a narrow angle pan/tilt camera that acquired higher resolution images of eyes. There exist many alternative methods for finding and tracking facial features such as the eyes, and this well researched topic will not be discussed further here. In these trials, most imaging was done without active pan/tilt camera optics, but instead exploited visual feedback via a mirror or video image to enable cooperating Subjects to position their own eyes within the field of view of a single narrow-angle camera.

Image focus assessment is performed in real time (faster than video frame rate) by measuring spectral power in middle and upper frequency bands of the 2-D Fourier spectrum of each image frame and seeking to maximize this quantity either by moving an active lens or by providing audio feedback to Subjects to adjust their range appropriately. The video rate execution speed of focus assessment (i.e., within 15 ms) is achieved by using a bandpass 2-D filter kernel requiring only summation and differencing of pixels, and no multiplications, within the 2-D convolution necessary to estimate power in the selected 2-D spectral bands. Images passing a minimum focus criterion are then analyzed to find the iris, with precise localization of its boundaries using a coarse-to-fine strategy terminating in single-pixel precision estimates of the center coordinates and radius of both the iris and the pupil. Although the results of the iris search greatly constrain the pupil search, concentricity of these boundaries cannot be assumed. Very often the pupil center is nasal, and inferior, to the ris center. Its radius can range from 0.1 to 0.8 of the iris radius. Thus, all three parameters defining the pupillary circle must be estimated separately from those of the iris. A very effective integrodifferential operator for determining these parameters is

(1)

 

where is an image containing an eye. The operator searches over the image domain (x,y) for the maximum in the blurred partial derivative with respect to increasing radius r, of the normalized contour integral I(x,y) of along a circular arc ds of radius r and center coordinates (x0, y0 ). The symbol * denotes convolution and G(r) is a smoothing function such as a Gaussian of scale o. The complete operator behaves as a circular edge detector, blurred at a scale set by o, searching iteratively for the maximal contour integral derivative at successively finer scales of analysis through the three parameter space of center coordinates and radius (x0,y0,r ) defining a path of contour integration.
The operator in (1) serves to find both the pupillary boundary and the outer (limbus) boundary of the iris, although the initial search for the limbus also incorporates evidence of an interior pupil to improve its robustness since the limbic boundary itself usually has extremely soft contrast when long wavelength NIR illumination is used. Once the coarse-to-fine iterative searches for both these boundaries have reached single-pixel precision, then a similar approach to detecting curvilinear edges is used to localize both the upper and lower eyelid boundaries. The path of contour integration in (1) is changed from circular to arcuate, with spline parameters fitted by statistical estimation methods to model each eyelid boundary. Images with less than 50% of the iris visible between the fitted eyelid splines are deemed inadequate, e.g., in blink. The result of all these localization operations is the isolation of iris tissue from other image regions, as illustrated by the graphical overlay on the eye.

How Iris Recognition Works - John Daugman, PhD, OBE, University of Cambridge