Non-Ideal Iris Segmentation Using Graph CutsShrinivas Pundlik, Damon Woodard and Stan BirchfieldClemson University, Clemson, SC USA Workshop on Biometrics, CVPR 2008

OutlineMotivationPrevious WorkProposed ApproachEyelash SegmentationTexture ComputationGraph Cuts for segmentationIris SegmentationIris RefinementResultsConclusion

Why Iris Recognition?Iris: a near ideal biometrichighly uniquestable over lifetimeRobust Recognition templates easy to store/encodefast and accurate matching algorithmsSecurityvery low false acceptsdifficult to spoof

Motivation for Iris SegmentationMatching Algorithms A typical iris recognition systemImage AcquisitionIris SegmentationRecognition errorsOcclusions ReflectionsBlurringPupil DilationOff-Axis GazeNon-Ideal ImagesIdeal ImagesMatching Algorithms Iris SegmentationSuccessful Recognition Segmentation is an important part of the larger iris recognition problem, especially when dealing with non-ideal images.

Previous Work[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

Overviewassign4 labels

PreprocessingRemoving Specular Reflections:Select pixels of high intensity valuesUn-paint these pixels and their immediate neighborsFind painted neighborsIteratively interpolate grayscale values until all pixels are paintedraw inputpreprocessed input

Texture Measures for Eyelash SegmentationTexture: amount of image intensity variation around a pixel neighborhoodTwo measures:Point featuresPoints with high intensity variation in both x and y directionsGradient magnitudeFeature points not enough due to sparsenessedges not considered as good point featuresEach not enough on its own

input imagefeature pointsgradient magnitudetexture map

Texture Computationpoint feature score for a pixel n :Construct a spatial histogram to compute texture scoresh(f) = min{e1, e2}, where e1 and e2 are the eigenvalues of G(f)num. of bins(constant)bin weight(constant)weight of inner circleweight of outer circlefeature point weight of feature fset of features in the inner histogramset of features in the outer histogramSpatial histogram centereda pixel n (shown as a blue dot)to compute point feature score

( is the neighborhood of feature f)

Texture Computation (Contd.)gradient magnitude score for a pixel n :num. of bins(constant)bin weight(constant)weight of inner circleweight of outer circlegradient magnitude at pixel jregion defined by the inner histogramregion defined by the outer histogram2r22r1Spatial histogram centereda pixel n (shown as a blue dot)to compute point feature score

Segmentation Using Graph Cuts[Boykov & Kolmogorov, PAMI 2004]segmentation = binary labelingEnergy minimization problem:E = Edata + Esmoothpenalty of assigning a label to a pixel( computed using the texture score )

penalty between two neighboring pixels(computed from grayscale image intensities)textured regions(eyelashes)untextured regions(rest of the eye)[Boykov, Veksler & Zabih, PAMI 2001]

Iris Segmentationassign a label to each pixel (iris, pupil or background) based on pixel intensitygrayscale histogram peaks represent the values of each labelgraph cuts used to obtain smooth segmented regionsEyelash SegmentationIris SegmentationGrayscale HistogramSmoothed HistogramInput Image

Iris Refinementsegmentation based on grayscale intensities may not be accuratecombine segmented iris region and a priori shape knowledgeuse pupil center to estimate iris boundary pointsleast square ellipse fitting

sampling irisboundary pointsinitial estimaterefined estimateellipse fittinginput imageiris mask

ResultsInput ImagePreliminary SegmentationIris Mask Ellipse FittingirisbackgroundpupileyelashesMaseks implementationof Daugmans algorithmour approach

More Results

Quantitative ComparisonComparison 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.

Conclusionintroduces a novel texture computation for eyelash segmentationuses 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 regionReduce the overall computation time of the algorithmValidate the segmentation procedure by performing iris recognition on known databases

Questions?