Transcript
IRIS RECOGNITION SYSTEM March 23, 2011 1
VIVEKANANDA COLLEGE OF ENGINEERING & TECHNOLOGY
Technical Seminar On
“IRIS BASED RECOGNITION SYSTEM USING WAVELET TRANSFORM”
byDECHAMMA I A(4VP07EC011)
VIII sem,EC
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IS FINGER PRINTING STRONG ENOUGH??
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IS VOICE RECOGNITION RELIABLE??
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IS DNA THE ULTIMATE??
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IS FACE RECOGNITION REALLY ROBUST??
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REAL AUTHENTICITY LIES IN
YOUR EYES…..
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Technical Seminar On
“IRIS BASED RECOGNITION SYSTEM USING WAVELET TRANSFORM”
byDECHAMMA I A(4VP07EC011)
VIII sem,EC
VIVEKANANDA COLLEGE OF ENGINEERING & TECHNOLOGY
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OVERVIEW OF PRESENTATION
Biometric System
Why IRIS?
Block Diagram
Advantages
Disadvantages
Conclusion
Reference
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Biometric System
A biometric system provides an automatic recognition of an individual based on some sort
of unique feature or characteristic possessed by the individual.
Something you know - password, PINSomething you have - smart card, token(secure ID card)Something you are - biometric
Security field uses three different types of authentication
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CATEGORIES OF BIOMETRICS
PHYSICAL BIOMETRICS1) Iris Recognition2) Retina Recognition3) Fingerprint Recognition4) Face Recognition5) Hand Scan6) Finger Scan
BEHAVIORAL BIOMETRICS1) Signature Recognition2) Voice Recognition3) Typing Recognition
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WHAT IS AN IRIS?
Coloured part of the eye
Contains delicate patterns
Texture is unique to a person
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Its suitability as an exceptionally accurate biometric
derives from its
extremely data-rich physical structure
genetic independence — no two eyes are the
same
patterns apparently stable throughout life
Highly protected by internal organ of the eye
Why IRIS?
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Anatomy of the Human Eye
Eye = Camera
Cornea bends, refracts, and focuses light.
Retina = Film for image projection (converts image into electrical signals).
Optical nerve transmits signals to the brain.
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Individuality of Iris
Left and right eye irises have distinctive pattern.
IRIS RECOGNITION?
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Most accurate biometric method
Uses pattern recognition techniques
Completely Non-Invasive
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Iris Recognition System
Feature Extraction by Haar Wavelet
Iris code
Normalization
WORKING
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Image Acqisition
Iris Localization
Iris Normalization
Feature Extraction
Matching
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I. Image Acquisition
Why important?
One of the major challenges of automated iris
recognition is to capture a high-quality image of the iris.
Concerns on the image acquisition rigs Obtain images with sufficient resolution and sharpness
Good contrast in the iris pattern with proper illumination
Well centered without unduly constraining the operator
Artifacts eliminated as much as possible
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Image Acquisition Device
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Purpose: To isolate the actual iris region in a digital
eye.
II. Iris Localization
Iris can be approximated by two circles, One for
iris/sclera boundary and another for iris/pupil boundary.
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How localization is done?
Pupil & Iris detection: Circular Hough Transform
xc^2 + yc^2 – r^2 = 0
xc & yc are centre coordinates of pupilr is radius of pupil
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III. Normalization
θ
θ
rr
0 1
θ
θ
Circular band is divided into 8 subbands of equal thickness for a given angle .
Subbands are sampled uniformly in and in r.
θ
θ
θ
θ
Rubber Sheet Model Each pixel (x,y) is mapped into pair of polar coordinates (r, ).
Where R is on interval (0,1) is angle (0,2pi)
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III. Feature Extraction
WAVELET ENCODING:-
Wavelet can be used to decompose the data in the iris region into components that appear at different resolution.
A number of wavelet filters is applied to 2D iris region, one for each resolution with each wavelet a scaled version of some basic function.
Output of applying wavelets (Haar Wavelet) is then encoded to provide iris pattern.
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HAAR WAVELET
Wavelet Transform breaks an image down into four sub-images.
Haar Wavelet Haar Transform
APPROACH:
Mapped image of size 100 x 402 pixels is decomposed to a max
of 5 levels
Levels are cd1 to cd5 in vertical, horizontal and diagonal
directions
Finally we get combinations of six matrices
Cd4(h) & Cd5(h)
Cd4(v) & Cd5(v)
Cd4(d) & Cd5(d)
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APPROACH:
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These matrices are combined to build one single vector called
Feature Vector.
Binary Coding Scheme:
To obtain vector in binary code.
Let “Coef” be Feature Vector of an image than the following
Quantization scheme coverts it to its code-word:
o If Coef(i) >= 0 then Coef(i)=1
o If Coef(i) < 0 then Coef(i)=0
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For matching, the Hamming distance was chosen as a metric for recognition.
The Daugman system computes the Hamming distance. The hamming distance between iris code X and Y is given by:
The decision whether two images belong to same person depends on the following result:
IV. Pattern Matching
If HD <= 0.32 decide that it is same person If HD > 0.32 decide that it is different person
An illustration of the shifting process. One shift is defined as one shift left, and one shift right of a reference template. In this example one filter is used to encode the templates, so only two bits are moved during a shift. The lowest Hamming distance, in this case zero, is then used since this corresponds to the best match between the two templates.
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Graphical User Interface
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Advantages:
Result is 99.9% accurate.
externally visible, so noninvasive — patterns imaged from a
distance.
Iris patterns possess a high degree of randomness .
Patterns apparently stable throughout life.
extremely data-rich physical structure.
It is a living password.
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Small target (1 cm) to acquire from a distance (1 m)
Cannot be applied to blind with impaired iris
Colored glasses must be avoided
Obscured by eyelashes, lenses, reflections
Deforms non-elastically as pupil changes size
Illumination should not be visible or bright
DISADVANTAGES:
APPLICATIONS:
Automatic Teller Machines(ATMs).
Tracking Prisoner Movement. Computer login. Premises access control. Personal certificates Forensics. Internet security.
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Conclusion:
Proven to be the most accurate and versatile security measure.
No room for human error
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J. Daugman, High confidence recognition of persons by test of statistical independence, IEEE Trans. Pattern Anal. Mach. Intell. 15
(11) (1993) 1148 – 1161. Jafar M.H.Ali and Aboul Ella Hassanien,”An Iris recognition system to
enhance E-security environment based on wavelet Theory”,AMO Journal,Volume 2
Daugman.J,”How iris recognition works,” IEEE trans Trans. On circuits and systems for video technolgy,vol.11.
www.wikipedia.org/iris regognition.html
Reference:
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