1. ANKUSH KUMAR (M.Tech(CS))
Iris Recognition
Using SURF. By
ANKUSH KUMAR (M.Tech(CS))
211CS2279
April 15, 2017
ANKUSH KUMAR

2. Contents Why Iris Recognition scores over others?
Anatomy of the Human Eye
The Iris
Iris Recognition : An Overview
The process
Segmentation
Normalization
Feature Encoding & Matching
Feature (Image Descriptor)
SURF
Matching Methods
Applications
References
April 15, 2017
ANKUSH KUMAR

3. Biometric Features Can be used to uniquely identify individuals Face
Fingerprint
Handprint
Voice
Iris
April 15, 2017
ANKUSH KUMAR

4. Why Iris Recognition scores over others?
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
April 15, 2017
ANKUSH KUMAR

5. 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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ANKUSH KUMAR

6. 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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7. Proposed Iris Recognition Systems
John Daugman (1993)
First and most well-known
Wildes (1996)
Boles (1998)
Ma (2004)
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ANKUSH KUMAR

8. Analysis & Recognition
Image
Capture
Iris
Segmentation
Feature
Extraction
Matching
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9. Typical iris system configuration
Uniform distribution
Stored templates
Reject
Pre processing
Feature-extraction
Identification Verification
Accept
Iris scan 2d image capture
Iris localization
Transform representation
comparison
enrolment
Authentication
April 15, 2017
ANKUSH KUMAR

10. Iris Recognition systems
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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ANKUSH KUMAR

11. Preprocessing 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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ANKUSH KUMAR

12. Iris Recognition System
Localization
Acquisition
IrisCode
Gabor Filters
Polar Representation
Image
Demarcated Zones
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ANKUSH KUMAR

13. Iris Segmentation
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)
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
April 15, 2017
ANKUSH KUMAR

14. Iris Localization Next, we must detect the outer boundary
Use canny edge detector and Hough transform
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15. 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
April 15, 2017
ANKUSH KUMAR

16. The normalization process for two images of the same iris taken under varying conditions
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17. Daugman’s Rubbersheet Model
Each pixel (x,y) is mapped into
polar pair (r, ).
Circular band is divided into 8
subbands of equal thickness for a given angle.
Subbands are sampled uniformly in and in r.
Sampling = averaging over a patch of pixels.
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ANKUSH KUMAR

18. Iris code generation
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19. Image Descriptor(feature)
SIFT(Scale Invariant Feature Transform)
GLOH (Gradient Location and Orientation Histogram)
HOG (Histogram of oriented gradients)
LESH (Local Energy based Shape Histogram)
SURF (Speeded Up Robust Feature)
Interest point detection
Descriptor
April 15, 2017
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20. April 15, 2017
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21. Detection Lxx(x,σ) is the Laplacian of Gaussian of the image
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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22. Detection cont…
Approximated second order derivatives with box filters (mean/average filter)
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23. Detection cont… Scale analysis with constant image size
9 x 9, 15 x 15, 21 x 21, 27 x  39 x 39, 51 x 51 …
1st octave 2nd octave
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24. Description Orientation Assignment
Circular neighborhood of radius 6s around the interest point
(s = the scale at which the point was detected)
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25. Description
DESCRIPTOR COMPONENT
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26. Matching
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)
April 15, 2017
ANKUSH KUMAR

27. Analysis SURF is good at SURF is poor at
handling serious blurring
handling image rotation
SURF is poor at
handling viewpoint change
handling illumination change
SURF describes image faster than SIFT by 3 times
SURF is not as well as SIFT on invariance to illumination change and viewpoint change
April 15, 2017
ANKUSH KUMAR

28. Matching Methods Hamming Distance
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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ANKUSH KUMAR

29. Matching Methods
Normalized Correlation
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30. An illustration of the feature encoding process.
ANKUSH KUMAR

31. 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.
April 15, 2017
ANKUSH KUMAR

32. April 15, 2017
ANKUSH KUMAR
Successful images

33. 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.
April 15, 2017
ANKUSH KUMAR

34. Pros
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).
It maintains stability of characteristic over a lifetime.
Iris has received little negative press and may therefore be more readily accepted. The fact that there is no criminal association helps.
The dominant commercial vendors claim that iris does not involve high training costs.
April 15, 2017
ANKUSH KUMAR

35. Cons
There are few legacy databases. Though iris may be a good biometric for identification, large-scale deployment is impeded by lack of installed base.
Since the iris is small, sampling the iris pattern requires much user cooperation or complex, expensive input devices.
The performance of iris authentication may be impaired by glasses, sunglasses, and contact lenses; subjects may have to remove them.
The iris biometric, in general, is not left as evidence on the scene of crime; no trace left.
April 15, 2017
ANKUSH KUMAR

36. 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
April 15, 2017
ANKUSH KUMAR

37. References
“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
Patch Descriptors, by :- Larry Zitnick
“SURF: Speeded Up Robust Features”. IEEE Explore By Herbert Bay.
April 15, 2017
ANKUSH KUMAR

38. THANKS
April 15, 2017
ANKUSH KUMAR