1. Non-Ideal Iris Segmentation Using Graph Cuts
Shrinivas Pundlik, Damon Woodard and
Stan Birchfield
Clemson University, Clemson, SC USA
Workshop on Biometrics, CVPR 2008

2. Outline Motivation Previous Work Proposed Approach Results Conclusion
Eyelash Segmentation
Texture Computation
Graph Cuts for segmentation
Iris Segmentation
Iris Refinement
Results
Conclusion

3. Why Iris Recognition? Iris: a near ideal biometric Robust Recognition
highly unique
stable over lifetime
Robust Recognition
templates easy to store/encode
fast and accurate matching algorithms
Security
very low false accepts
difficult to spoof

4. Motivation for Iris Segmentation
A typical iris recognition system
Occlusions
Image Acquisition
Reflections
Ideal
Images
Non-Ideal
Images
Blurring
Pupil
Dilation
Iris
Segmentation
Iris
Segmentation
Off-Axis
Gaze
extensively studied
Matching
Algorithms
Matching
Algorithms
challenging!
Recognition errors
Successful Recognition
Segmentation is an important part of the larger iris recognition problem, especially when dealing with non-ideal images.

5. Previous Work Common Themes:
[Ma et al. 2003]
Fourier transforms
[Kong & Zhang 2003]
image intensity differences
[Daugman 2003]
integro-differential approach
[Kang & Park 2007]
focus variance
[Bachoo & Tapamo]
Gray Level Co-occurrence Matrix (GLCM)
[Daugman 2007]
active contours
[Ross et al. 2006]
geodesic active contours
Common Themes:
require ideal iris images for satisfactory performance
primarily use eye geometry

6. Overview - assign 4 labels Input Iris Image Preprocessed input
Specular Reflections
Eyelash Segmentation
Iris Mask -
Iris Segmentation
Iris Refinement
Iris Ellipse
assign
4 labels

7. Preprocessing Removing Specular Reflections:
Select pixels of high intensity values
“Un-paint” these pixels and their immediate neighbors
Find “painted” neighbors
Iteratively interpolate grayscale values until all pixels are “painted”
raw input
preprocessed input

8. Texture Measures for Eyelash Segmentation
Texture: amount of image intensity variation around a pixel neighborhood
Two measures:
Point features
Points with high intensity variation in both x and y directions
Gradient magnitude
Feature points not enough due to sparseness
edges not considered as good point features
Each not enough on its own
input image
feature points
gradient magnitude
texture map

9. Texture Computation Construct a spatial histogram to
compute texture scores
point feature score for a pixel n :
feature point weight of feature f
Spatial histogram centered
a pixel n (shown as a blue dot)
to compute point feature score
num. of bins
(constant)
bin weight
(constant)
weight of inner circle
weight of outer circle
set of features in
the inner histogram
set of features in
the outer histogram
h(f) = min{e1, e2}, where e1 and e2 are the eigenvalues of G(f)
( is the neighborhood of feature f)

10. Texture Computation (Contd.)
gradient magnitude score for a pixel n :
gradient magnitude at pixel j
num. of bins
(constant)
bin weight
(constant)
2r1
weight of inner circle
weight of outer circle
2r2
region defined by
the inner histogram
region defined by
the outer histogram
Spatial histogram centered
a pixel n (shown as a blue dot)
to compute point feature score
, where Ix and Iy are the image gradients
in the x and y directions.
Combined texture score for a pixel n:
point feature score
gradient score

11. Segmentation Using Graph Cuts
[Boykov & Kolmogorov, PAMI 2004]
[Boykov, Veksler & Zabih, PAMI 2001]
textured regions
(eyelashes)
segmentation = binary labeling
untextured regions
(rest of the eye)
Energy minimization problem:
E = Edata Esmooth
penalty of assigning
a label to a pixel
( computed using
the texture score )
penalty between two
neighboring pixels
(computed from grayscale
image intensities)

12. Iris Segmentation
assign a label to each pixel (iris, pupil or background) based on pixel intensity
grayscale histogram peaks represent the values of each label
graph cuts used to obtain smooth segmented regions
Input Image
Grayscale Histogram
Smoothed Histogram
Eyelash Segmentation
Iris Segmentation

13. Iris Refinement
segmentation based on grayscale intensities may not be accurate
combine segmented iris region and a priori shape knowledge
use pupil center to estimate iris boundary points
least square ellipse fitting
input image
sampling iris
boundary points
initial estimate
refined estimate
ellipse fitting
iris mask

14. Preliminary Segmentation
Results
Input Image
Preliminary Segmentation
Iris Mask
Ellipse Fitting
Masek’s implementation
of Daugman’s algorithm
pupil
iris
eyelashes
background
our approach

15. More Results

16. Quantitative Comparison
Iris Parameter
Our approach
Masek’s Implementation1
Average Error
(in pixels)
Standard Deviation
Center (x)
1.9
2.0
6.8
17.4
Center (y)
2.8
2.1
3.3
5.5
Radius (x)
3.9
2.4
4.4
3.5
Radius (y)
3.4
4.9
2.7
Comparison of the estimated iris region ellipse parameters with the ground truth data for 60 images from the WVU non-ideal iris image database. The ground truth was obtained by manually marking the iris region.
1L.Masek and P. Kovesi. Matlab source code for biometric identification system based on iris patters. The School of Computer Science and Software Engineering, The University of Western Australia, 2003.

17. Conclusion
introduces a novel texture computation for eyelash segmentation
uses graph cuts to densely and explicitly segment eyelashes, iris, pupil and background
comparison with the ground truth demonstrates the accuracy of segmentation
Future work:
Handle multi-modal intensity distribution in the iris region
Reduce the overall computation time of the algorithm
Validate the segmentation procedure by performing iris recognition on known databases

18. Questions?