Transcript Slide 1

Iris Recognition
Using SURF.
By
ANKUSH KUMAR (M.Tech(CS))
211CS2279
July 21, 2015
ANKUSH KUMAR
Contents

Why Iris Recognition scores over others?
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Anatomy of the Human Eye
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The Iris
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Iris Recognition : An Overview
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The process
◦ Segmentation
◦ Normalization
◦ Feature Encoding & Matching
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Feature (Image Descriptor)
◦ SURF
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Matching Methods
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Applications
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References
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Biometric Features
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Can be used to uniquely identify
individuals
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Face
Fingerprint
Handprint
Voice
Iris
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Why Iris Recognition scores over others?
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Has highly distinguishing texture
Right eye differs from left eye
Twins have different iris texture
Not trivial to capture quality image
+ Works well with cooperative subjects
+ Used in many airports in the world
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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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Iris
Iris is the area of the eye where the pigmented or coloured
circle, usually brown, blue, rings the dark pupil of the eye.
Example of 10 Different People Iris
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Proposed Iris Recognition Systems
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John Daugman (1993)
◦ First and most well-known
Wildes (1996)
 Boles (1998)
 Ma (2004)
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Analysis & Recognition
Image
Capture
Iris
Segmentation
Feature
Extraction
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Matching
Typical iris system configuration
Uniform
distribution
Pre
processing
Iris scan 2d image
capture
Iris
localization
enrolment
Stored
templates
Reject
Featureextraction
Identification
Verification
Transform
representation
Accept
comparison
Authentication
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Iris Recognition systems
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The iris-scan process begins with a photograph. A specialized
camera, typically very close to the subject, not more than three
feet, uses an infrared image to illuminate the eye and capture a
very high-resolution photograph.This process takes 1 to 2
seconds.
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Preprocessing
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Image acquisition
- Focus on high resolution and quality
- Moderate illumination
- Elimination of artifacts
Image localization
Adjustments for imaging contrast, illumination
and camera gain
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Iris Recognition System
Acquisition
IrisCode
Gabor Filters
Image
Localization
Polar Representation
Demarcated Zones
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Iris Segmentation
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Objective : To isolate the actual iris region in a digital eye
image
Can be approximated by two circles
the iris/sclera boundary
the iris/pupil boundary(interior to former)
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Depends on the image quality
Ex :- persons with darkly pigmented irises will present very
low contrast between the pupil and iris region if imaged
under natural light
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Iris Localization
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Next, we must detect the outer boundary
Use canny edge detector and Hough transform
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Normalization
• Once the iris region is successfully segmented
from an eye image, the next stage is to transform
the iris region so that it has fixed dimensions in
order to allow comparisons
• The normalization process will produce iris
regions, which have the same constant dimensions
• Two photographs of the same iris under different
conditions will have characteristic features at the
same spatial location
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The normalization process for two images of the same
iris taken under varying conditions
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Daugman’s Rubbersheet Model
θ
r
0
1
Each pixel (x,y) is mapped into
polar pair (r, θ ).
r
θ
Circular band is divided into 8
subbands of equal thickness for
a given θ angle.
Subbands are sampled
uniformly in θ and in r.
Sampling = averaging over a
patch of pixels.
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Iris code generation
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Image Descriptor(feature)
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SIFT(Scale Invariant Feature Transform)
◦ GLOH (Gradient Location and Orientation Histogram)
◦ HOG (Histogram of oriented gradients)
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LESH (Local Energy based Shape Histogram)
SURF (Speeded Up Robust Feature)
◦ Interest point detection
◦ Descriptor
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Detection
• Hessian-based interest point localization
• Lxx(x,σ) is the Laplacian of Gaussian of the image
• It is the convolution of the Gaussian second order derivative with the
image
• Lindeberg showed Gaussian function is optimal for scale-space
analysis
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Detection cont…
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Approximated second order derivatives with box
filters (mean/average filter)
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Detection cont…
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Scale analysis with constant image size
9 x 9, 15 x 15, 21 x 21, 27 x 27
1st octave
39 x 39, 51 x 51 …
2nd octave
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Description
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Orientation Assignment
Circular neighborhood of radius 6s around the interest point
(s = the scale at which the point was detected)
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Description
DESCRIPTOR COMPONENT
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Matching
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Fast indexing through the sign of the Laplacian for the underlying
interest point
The sign of trace of the Hessian matrix
– Trace = Lxx + Lyy
Either 0 or 1 (Hard thresholding, may have boundary effect …)
In the matching stage, compare features if they have the same
type of contrast (sign)
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Analysis
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SURF is good at
◦ handling serious blurring
◦ handling image rotation
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SURF is poor at
◦ handling viewpoint change
◦ handling illumination change
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SURF describes image faster than SIFT by 3 times
SURF is not as well as SIFT on invariance to illumination
change and viewpoint change
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Matching Methods
Hamming Distance
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The Hamming distance gives a measure of how many bits are the same
between two bit patterns.
In comparing the bit patterns X and Y, the Hamming distance, HD, is defined
as the sum of disagreeing bits (sum of the exclusive-OR between X and Y)
over N, the total number of bits in the bit pattern.
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Matching Methods
Normalized Correlation
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An illustration of the feature encoding process.
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An illustration of the shifting process.
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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Successful images
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Observations
• Two IrisCodes from the same eye
form genuine pair => genuine
Hamming distance.
• Two IrisCodes from two different
eyes form imposter pair =>
imposter Hamming distance.
• Bits in IrisCodes are correlated
(both for genuine pair and for
imposter pair).
• The
correlation
between
IrisCodes from the same eye is
stronger.
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Pros
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Iris is currently claimed and perhaps widely believed to be the most
accurate biometric, especially when it comes to FA rates. Iris has very
few False Accepts (the important security aspect).
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It maintains stability of characteristic over a lifetime.
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Iris has received little negative press and may therefore be more
readily accepted. The fact that there is no criminal association helps.
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The dominant commercial vendors claim that iris does not involve
high training costs.
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Cons
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There are few legacy databases. Though iris may be a good biometric
for identification, large-scale deployment is impeded by lack of
installed base.
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Since the iris is small, sampling the iris pattern requires much user
cooperation or complex, expensive input devices.
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The performance of iris authentication may be impaired by glasses,
sunglasses, and contact lenses; subjects may have to remove them.
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The iris biometric, in general, is not left as evidence on the scene of
crime; no trace left.
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Conclusion
The iris is an ideal biometric feature for human
identification
 Although relatively young, the field of iris
recognition has seen some great successes
 Commercial implementations could become
much more common in the future
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References
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“How iris recognition works” by J. Daugman, Proceedings
of 2002 International Conference on Image Processing,
Vol. 1, 2002.
“Recognition of Human Iris Patterns for Biometric Identification”
by Libor Masek, The University of Western Australia, 2003
http://en.wikipedia.org/wiki/SURF
Patch Descriptors, by :-Larry Zitnick ([email protected])
“SURF: Speeded Up Robust Features”. IEEE Explore By
Herbert Bay.
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THANKS
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