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An Efficient Algorithm for Iris Pattern

Published on Dec 02, 2015


Wavelet analysis have received significant attention because their multi-resolution decomposition allows efficient image analysis. It is widely used for varied applications such as noise reduction, and data compression, etc. In this paper we have introduced and applied the concept of 2 dimensional Gabor wavelet transform to Biometric Iris recognition system.

The application of this transform in encoding the iris image for pattern recognition proves to achieve increased accuracy and processing speed compared to other methods. With a strong scientific approach and mathematical background we have developed an algorithm to facilitate the implementation of this method under the platforms of MATLAB

IMAGES - An introduction:

A dictionary defines image as a "reproduction or representation of the form of a person or thing". The inherent association of a human with the visual senses, predisposes one to conceive an image as a stimulus on the retina of the eye, in which case the mechanism of optics govern the image formation resulting in continuos range, multi-tone images.

A digital image can be defined to be a numerical representation of an object or more strictly to be sampled, quantized function of two dimensions which has been generated by optical means, sampled in an equally spaced rectangular grid pattern, and quantized in equal intervals of graylevel.

The word is crying out for the simpler access controls to personal authentication systems and it looks like biometrics may be the answer. Instead of carrying bunch of keys, all those access cards or passwords you carry around with you, your body can be used to uniquely identify you. Furthermore, when biometrics measures are applied in combination with other controls, such as access cards or passwords, the reliability of authentication controls takes a giant step forward.


Biometrics is best defined as measurable physiological and/or behavioral characteristics that can be utilized to verify the identity of an indivisual. They include the following:

" Iris scanning

" Facial recognition

" Fingerprint verification

" Hand geometry

" Retinal scanning

" Signature verification

" Voice verification


" Highly protected internal organ of the eye.

" Iris patterns possess a high degree of randomness.

" Variability: 244 degrees of freedom.

" Entropy: 3.2 bits per square millimetre.

" Uniqueness: set by combinatorial complexity.

" Patterns apparently stable throughout life.

IRIS - An introduction:

The iris is a colored ring that surrounds the pupil and contains easily visible yet complex and distinct combinations of corona, pits, filaments, crypts, striations, radial furrows and more.

The iris is called the "Living password" because of its unique, random features. It's always with you and can't be stolen or faked. As such it makes an excellent biometrics identifier.


Iris scanning is undoubtedly the less intrusive of the eye related biometrics. It utilizes a fairly conventional CCD camera element and requires no intimate contact between user and reader.

The iris scanning procedures as used on humans is simple and painless. The person stands a foot or so away from the camera and looks into the scanning device. In iris scanning, the eye is illuminated by the light emitting diodes that surround the camera. The diodes emit in the visible light spectrum. The scanner is not a laser-rectinal scanner so there are no laser hazards. It scans a a high-definition photograph of the person’s eyes (i.e., some of the characteristics of the iris tissue, such as rings, furrows and freckles are scanned). It then analyzes 266 different points of data (512-byte biometrics template) from the trabecular meshwork of the iris.The scanned pattern is then digitized and compared to previously recorded patterns. Identification is achieved in two seconds and verification in three.


Wavelet transforms have received significant attention because their multiresolution decomposition allows efficient image analysis, noise reduction and data compression. Using Discrete wavelet transform (DWT), the procedures of terrain segmentation and speckle noise reduction can be effectively combined into a single process. We have applied Gabor Wavelet transform in our algorithm to achieve iris image pattern recognition.

We have divided our entire Algorithm into three parts






Iris Image from a CCD camera and SSN of the user.


Iris code for Iris Image.


1) Test the resolution of the captured image.

2) Convert the RGB image to the monochrome format.

3) Locate the Iris in the image.

4) Encode the iris pattern to the corresponding grayscale by using circular grid.

5) Each isolated iris pattern is then demodulated to extract its phase information using quadrature 2D Gabor wavelets.

6) A 512-byte bar code is generated based on the distribution of black and white areas of the iris inside the grid.

7) Store the iris code and the SSN in the source folder.

Algorithm in Detail:

Testing the resolution:

The image captured by a CCD camera (480 x 640) should be converted to RGB to monochrome format. It is then resolved to a minimum of 70 pixels in iris radius for the rich details of iris patterns. Now a days 100 to 140 pixels can be resolved easily.

Locating Iris in the Image:

To locate the iris in the image the center coordinates and radius of both the iris and the pupil are determined using the operator explained below. A CCD camera captures the image of the eye. 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 acquires higher resolution images of eyes. In these trails, 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 fields of view of a single narrow-angle camera.

Focus assessment is performed in real-time (faster than video frame rate) by measuring the total high-frequency power in the 2D Fourier spectrum of each 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. Images passing a minimum focus criterion were 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 iris center.

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