IRIS RECOGNITION

SYSTEM

By: Nileshwari Desai

Roll No: A 216

Outline

• Introduction

• History

• Features

• Database design

• Identification steps

• Feature Extraction

• Matching

• Performance Evaluation

• Advantages

• Concerns/possible improvements

• Disadvantages

• Conclusion

• References

Introduction

• Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on the images of the irides of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.

• Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures of the iris externally visible at the front of the eye.

• Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.

History

• The concept of Iris Recognition was first proposed by Dr.

Frank Burch in 1939.

• It was first implemented in 1990 when Dr. John Daugman

created the algorithms for it.

• These algorithms employ methods of pattern recognition

and some mathematical calculations for iris recognition.

• The remarkable story of Sharbat Gula, first photographed in 1984 aged 12 in a

refugee camp in Pakistan by National Geographic (NG) photographer Steve

McCurry, and traced 18 years later to a remote part of Afghanistan where she was

again photographed by McCurry.

• So the NG turned to the inventor of automatic iris recognition, John Daugman at

the University of Cambridge.

John Daugman and the Eyes of Sharbat

Gula

The identifiable features include:

• Furrows

• Coronas

• Stripes

• Striations

• Color of the iris

• Collagenous fibers

• Filaments

• Crypts (darkened areas on the iris)

• Serpentine vasculature

• Pupil ring

• Freckles

Database design

Universality

The iris of the eye has been described as the ideal part of the human body for biometric identification for several reasons:

• It is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea ). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor. The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil.

• Everybody in the world possess eyes, even the blind person would have an iris. Blindness would only ruin the retina and not the iris. Thus, Iris can be considered as universal.

Uniqueness

• Every human being have unique iris pattern. Even two identical twins have different

irises.

Permanence

• Most of the time, people's eyes also remain unchanged after eye surgery, and blind

people can use iris scanners as long as their eyes have irises.

• Even after laser surgery or cataract operation, a person’s iris won’t change for at

least 10 years.

• People's retinas change as they age and not the iris, which helps not to lead to

inaccurate readings.

Robustness

• It should not change with time. Iris is a part of the body which does not change over

until 50 years of age.

Performance

• The performance of the system can be predicted only after gathering all the data

and running FAR, FRR like tests on them. Mostly the system is robust and gives

accurate results.

User’s acceptability

• Iris scanning can seem very futuristic, but at the heart of the system is a

simple CCD digital camera. It uses both visible and near-infrared light to

take a clear, high-contrast picture of a person's iris. Some people confuse

iris scans with retinal scans. Retinal scans, however, are an older

technology that required a bright light to illuminate a person's retina. The

sensor would then take a picture of the blood vessel structure in the back of

the person's eye. Some people found retinal scans to be uncomfortable and

invasive. People's retinas also change as they age, which could lead to

inaccurate reading.

Collectability

• It is easy to collect the samples. When you look into an iris scanner, your

eye is 3 to 10 inches from the camera. When the camera takes a picture, the

computer locates

-The center of the pupil

-The edge of the pupil

-The edge of the iris

-The eyelids and eyelashes

It then analyzes the patterns in the iris and translates them into a code.

Database collected

• The database has been downloaded/taken from the

CASIA iris image database which is easily accessible. The

version taken is CASIA V2.

• The website link is as follows:-

http://biometrics.idealtest.org/dbDetailForUser.do?id=4

• The irises were scanned by TOPCON TRC50IA optical

device connected with SONY DXC- 950P 3CCD camera.

Parameter Quantity

Total images per person 10

Total number of individuals 20

Total images in the database for left eye

200

Total images in the database for right eye

200

Total database

400

Identification steps

• Localization - The inner and the outer boundaries of the

iris are calculated.

• Normalization - Iris of different people may be captured in

different size, for the same person also size may vary

because of the variation in illumination and other factors.

• Feature extraction - Iris provides abundant texture

information. A feature vector is formed which consists of

the ordered sequence of features extracted from the

various representation of the iris images.

• Matching - The feature vectors are classified through

different thresholding techniques like Euclidean distance,

Hamming Distance, weight vector and winner selection,

dissimilarity function, etc.

Feature extraction

Iris localization

Iris boundaries localization

approximate pupil center detection

Iris boundary points

detection

Curve fitting Eye lid

detection

Iris localization

Localized iris boundaries

(a). Using AIPF method.

(b). Using integrodifferential method

(a) (b)

Normalization

I(x,y) is the iris region image, (x,y) and (r,θ) are the cartesian and normalised polar

coordinates respectively, (xp, yp ) and (xi, yi) are the coordinates of pupil and iris

boundaries along θ direction.

(R, θ) to unwrap iris and easily generate a template code.

Encoding- Gabor filter

Gabor filters provide excellent attributes which are suitable to

extract iris features.

σx , σy are the scale parameters of guassian function,

µ, v are frequency parameters of gabor fliter.

Matching

• Euclidean distance has been used to perform matching.

• The database image which gives least Euclidean distance

is identified to belong to the genuine user.

• Matching can also be done by hamming distance, weight

vector, winner selection and dissimilarity function for iris

recognition system.

Performance evaluation

• FAR: measurement of how many imposter users are falsely accepted into the system as “genuine” users.

• FRR: measurement of how many genuine users are falsely rejected by the system as “imposters”.

• GAR: overall accuracy, measurement of how many genuine users are accepted into the system as “genuine” users.

• GRR: measurement of how many genuine users are rejected by the system as “imposters” because of some noise present.

Advantages

• Uniqueness of iris patterns hence improved accuracy.

• Highly protected, internal organ of the eye.

• Stability : Persistence of iris patterns.

• Non-invasive : Relatively easy to be acquired.

• Smaller template size so large databases can be easily

stored and checked.

• Cannot be easily forged or modified.

Concerns / Possible improvements

• Person has to be “physically” present.

• Capture images independent of surroundings and environment / Techniques for dark eyes.

• Non-ideal iris images.

Pupil dilation Eye rotation Inconsistent iris size

Disadvantages

• It will be difficult to capture an image of handicap people

sitting on wheel chair because the cameras are usually

attached on the wall and capture an image up to a certain

height.

• The iris recognition systems are much costlier than other

biometric technologies.

• If a person is wearing glasses or facing direct sunlight for

quite a while, than it may affect the authentication.

Conclusion

• The applications of iris recognition are rapidly growing in

the field of security, due to it’s high rate of accuracy. This

technology has the potential to take over all other security

techniques, as it provides an hands-free, rapid and

reliable identification process.

References

1. J. Daugman’s web site. URL: http://www.cl.cam.ac.uk/users/jgd1000/

2. J. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148 – 1161, 1993.

3. J. Daugman, United States Patent No. 5,291,560 (issued on March 1994). Biometric Personal Identification System Based on Iris Analysis, Washington DC: U.S. Government Printing Office, 1994.

4. J. Daugman, “The Importance of Being Random: Statistical Principles of Iris Recognition,” Pattern Recognition, vol. 36, no. 2, pp 279-291.

5. R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proc. of the IEEE, vol. 85, no. 9, 1997, pp. 1348-1363.