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17 April 2006 Enhanced iris matching using estimation of in-plane nonlinear deformations
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Like many visual patterns, captured images from the same iris biometric experience relative nonlinear deformations and partial occlusions. These distortions are difficult to normalize for when comparing iris images for match evaluation. We define a probabilistic framework in which an iris image pair constitute observed variables, while parameters of relative deformation and occlusion constitute unobserved latent variables. The relation between these variables are specified in a graphical model, allowing maximum a posteriori probability (MAP) approximate inference in order to estimate the value of the hidden states. To define the generative probability of the observed iris patterns, we rely on the similarity values produced by correlation filter outputs. As a result, we are able to develop an algorithm which returns a robust match metric at the end of the estimation process and works reasonably quickly. We show recognition results on two sets of real iris images: the CASIA database, collected by the Chinese Academy of Sciences, and a database collected by the authors at Carnegie Mellon University.
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Jason Thornton, Marios Savvides, and B. V. K. Vijaya Kumar "Enhanced iris matching using estimation of in-plane nonlinear deformations", Proc. SPIE 6202, Biometric Technology for Human Identification III, 62020E (17 April 2006);


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