Titlebook: Handbook of Iris Recognition; Kevin W. Bowyer,Mark J. Burge Book 2016Latest edition Springer-Verlag London 2016 Biometrics.Computer Vision

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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

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Design Decisions for an Iris Recognition SDK,. In addition, for further advances in the field of iris biometrics the community needs to be provided with state-of-the-art reference systems, which serve as adequate starting point for new research. This chapter provides a summary of relevant design decisions for software modules constituting an i

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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