4. 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.
5. 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.
6. • 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.
8. 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
9. 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.
10. 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.
11. 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.
12. 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.
13.
14. 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
15. 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.
19. 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.
20. (R, θ) to unwrap iris and easily generate a template code.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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
26. 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.
27. 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.
28. 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.
