Final Project Seminar on Iris Recognition by Bharath.m
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Transcript of Final Project Seminar on Iris Recognition by Bharath.m
IRIS RECOGNITION AS A STRONG BIOMETRIC CUE
FOR IDENTIFICATION
By,Arun Ross, West Virginia University,IEEE CS Workshop on Biometrics (CVPRW06),IEEE CS Press.2009.
Iris Detection - Bharath.M [H.K.B.K.C.E] 1
Outline• The Present Existing Systems. • What Is Iris Recognition System ?• Anatomy Of The Human Eye.• Schematic Diagram Of Iris Detection.• 3 Stages -> Image Acquisition. -> Image Localization. -> Pattern Matching.• Advantages and drawbacks.• Special Case Solved• Reference.
Iris Detection - Bharath.M [H.K.B.K.C.E] 2
The Present Existing System
1) Finger Print recognition system.
Iris Detection - Bharath.M [H.K.B.K.C.E] 3
2) Voice recognition system.
3) Face recognition system.
4) Hand geometry recognition system.
What Is Iris Recognition System ?
Iris recognition is a method of biometric authentication that uses pattern-recognition techniques based on high-resolution images of the irides of an individual's eyes.
“Iris is unique to an individual”-is the conclusion of ophthalmologists and anatomists, Therefore recognition of separate individuals is possible.
Iris Detection - Bharath.M [H.K.B.K.C.E] 4
Anatomy of the
Human Eye
• Eye=Camera.
• Cornea bends, refracts, and focuses light.
• Retina = Film for image projection (converts image into electrical signals).
• Pupil allows light to enter the retina.
• Iris = Aperture Iris Detection - Bharath.M [H.K.B.K.C.E] 5
Schematic diagram of iris recognition
3-STAGES
Iris Detection - Bharath.M [H.K.B.K.C.E] 6
Usage of iris scanners.
Usage of infra red beam.
One of the major challenges of automated iris recognition is to capture a high-quality image of the iris while remaining noninvasive to the human operator.
Image Acquisition
Iris Detection - Bharath.M [H.K.B.K.C.E] 7
John Daugman (1994)
• Pupil detection: circular edge detector
• Segmenting sclera
0000
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yxI
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Iris Localization before
after
Iris Detection - Bharath.M [H.K.B.K.C.E] 8
Rubbersheet Model
rr
0 1
θ
θ
Each pixel (x,y) is mapped into polar pair (r, ).
Circular band is divided into 8 subbands of equal thickness for a given angle .
θ
θ
Mapping of the Co-ordinates.
Iris Detection - Bharath.M [H.K.B.K.C.E] 9
Representation of iris and also of a person
Textured region is unique for a person
Storing Of Image Data In Term Of Bytes.
The Bar-Code of an individual
Iris Detection - Bharath.M [H.K.B.K.C.E] 10
Pattern Matching The stored image bar code along with its bit
data is compared.
Graph showing a high degree of success rate in iris pattern matching
Iris Detection - Bharath.M [H.K.B.K.C.E] 11
ADVANTAGES OF I.R.S
Stable. Unique. Flexible. Reliable. Non-Invasive. Unprecedented false match rate. Fastest scan and save time.
Iris Detection - Bharath.M [H.K.B.K.C.E] 12
Costly.
Non-differentiation.
Moving target.
Not applicable to total blind.
Surgery.
Drawbacks Of I.R.S
Iris Detection - Bharath.M [H.K.B.K.C.E] 13
LOST AND FOUND
[A SPECIAL CAS
E OF I.R.S]
Iris Detection - Bharath.M [H.K.B.K.C.E] 14
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.
Iris Detection - Bharath.M [H.K.B.K.C.E] 15
By,Bharath.M1HK06CS012
THANK YOU !ANY QUESTIONS ?
Iris Detection - Bharath.M [H.K.B.K.C.E] 16