05-02-2013, 11:24 AM
IRIS RECOGNITION
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Abstract
In this paper, we describe the novel techniques
we developed to create an Iris Recognition System, in
addition to an analysis of our results. We used a fusion
mechanism that amalgamates both, a Canny Edge
Detection scheme and a Circular Hough Transform, to
detect the iris’ boundaries in the eye’s digital image. We
then applied the Haar wavelet in order to extract the
deterministic patterns in a person’s iris in the form of a
feature vector. By comparing the quantized vectors using
the Hamming Distance operator, we determine finally
whether two irises are similar. Our results show that our
system is quite effective.
INTRODUCTION
The purpose of ‘Iris Recognition’, a biometrical
based technology for personal identification and
verification, is to recognize a person from his/her iris
prints. In fact, iris patterns are characterized by high level
of stability and distinctiveness. Each individual has a
unique iris (see Figure 1); the difference even exists
between identical twins and between the left and right
eye of the same person. [6]
Image acquisition
Image acquisition is considered the most critical
step in our project since all subsequent stages depend
highly on the image quality. In order to accomplish this,
we used a CCD camera. We set the resolution to
640x480, the type of the image to jpeg, and the mode to
white and black for greater details. Furthermore, we took
the eye pictures while trying to maintain appropriate
settings such as lighting and distance to camera.
Image manipulation
In the preprocessing stage, we transformed the
images from RGB to gray level and from eight-bit to
double precision thus facilitating the manipulation of the
images in subsequent steps.
Iris localization
Before performing iris pattern matching, the
boundaries of the iris should be located. In other words,
we are supposed to detect the part of the image that
extends from inside the limbus (the border between the
sclera and the iris) to the outside of the pupil [6]. We start
by determining the outer edge by first downsampling the
images by a factor of 4, to enable a faster processing
delay, using a Gaussian Pyramid. We then use the Canny
operator with the default threshold value given by
Matlab, to obtain the gradient image.
Mapping
After determining the limits of the iris in the
previous phase, the iris should be isolated and stored in a
separate image. The factors that we should watch out for
are the possibility of the pupil dilating and appearing of
different size in different images. For this purpose, we
begin by changing our coordinate system by unwrapping
the lower part of the iris (lower 180 degrees) and
mapping all the points within the boundary of the iris into
their polar equivalent (Figures 3 & 4). The size of the
mapped image is fixed (100x402 pixels) which means
that we are taking an equal amount of points at every
angle. Therefore, if the pupil dilates the same points will
be picked up and mapped again which makes our
mapping process stretch invariant.
When unwrapping the image, we make use of
the bilinear transformation to obtain the intensities of the
points in the new image. The intensities at each pixel in
the new image are the result of the interpolation of the
grayscales in the old image. [4]
CONCLUSION
We have successfully developed a new Iris
Recognition system capable of comparing two digital
eye-images. This identification system is quite simple
requiring few components and is effective enough to be
integrated within security systems that require an
identity check. The errors that occurred can be easily
overcome by the use of stable equipment.