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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Iris Quality Metrics for Adaptive Authentication, at the acquisition stage, at image enhancement stage, or at matching and fusion stage. Metrics designed to evaluate iris sample quality are used as figures of merit to quantify degradations in iris images due to environmental conditions, unconstrained presentation of individuals or due to postproce

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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 challenges associated with the segmentation process, along with some of the prominent iris segmentation techniques proposed in the literature. The methods are presente

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Iris Recognition with Taylor Expansion Features,r we describe three iris representations. The first one is a phase-based iris texture representation which is based on a binarized multi-scale Taylor expansion. The second one describes the iris by using the most significant local extremum points of the first two Taylor expansion coefficients. The t

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Multispectral Iris Fusion and Cross-Spectrum Matching, discomfort from illumination, reduces specular reflections, and increases the amount of texture captured for some iris colors. However, previous research has shown that matching performance is not invariant to iris color and can be improved by imaging outside the NIR spectrum. Building on this rese

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