The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
A study of Iris Recognition technology over the in use biometric technologies these days. These Study shows how beneficial the iris technology can be to the Human in future.
I have put all my efforts in this study and have made an simple easy to understand ppt.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
A study of Iris Recognition technology over the in use biometric technologies these days. These Study shows how beneficial the iris technology can be to the Human in future.
I have put all my efforts in this study and have made an simple easy to understand ppt.
Keystroke dynamics, or typing dynamics, is the detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of
the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
False Reject Rate is 10%.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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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.