Titlebook: Handbook of Iris Recognition; Mark J. Burge,Kevin W. Bowyer Book 20131st edition Springer-Verlag London 2013 Biometrics.Computer Vision.Im

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Quality and Demographic Investigation of ICE 2006,l Biometric Group, the Iris 2006 evaluation conducted by Authenti-Corp, and the Iris Challenge Evaluation (ICE) 2006 and Iris Exchange (IREX) conducted by the National Institute of Standards and Technology. These experimental evaluations employed different vendor technologies and experimental specif

moratorium

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A Theoretical Model for Describing Iris Dynamics,tion algorithms assume a simple transformation to approximate the deformation of the iris tissue. Furthermore, current research work on iris deformation does not take into account the mechanical properties of the iris tissue nor the cause of deformation from the iris muscle activity. By looking at t

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Methods for Iris Segmentation,its. Such a performance is possible by accurately segmenting the iris region from the given ocular image. This chapter discusses the various challenges associated with the segmentation process, along with some of the prominent iris segmentation techniques proposed in the literature. Furthermore, met

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Iris Segmentation for Challenging Periocular Images,the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, defocus blur, motion blur, and low resolution. The five algorithms considered in this regard are based on the (a) integrodifferential operator, (b) Hough

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Periocular Recognition from Low-Quality Iris Images,been acquired with sufficient quality to reliably compute an IrisCode, the periocular region can provide additional discriminative information for biometric identification. The NIR periocular images which form NIST’s Face and Ocular Challenge Series (FOCS) are characterized by large variations in il