Abstract
Iris sample quality has a number of important applications. It can be used at a variety of processing levels in iris recognition systems, for example, 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 postprocessing that can reduce iris information in the data. This chapter presents a short summary of quality factors traditionally used in iris recognition systems. It further introduces new metrics that can be used to evaluate iris image quality. The performance of the individual quality measures is analyzed, and their adaptive inclusion into iris recognition systems is demonstrated. Three methods to improve the performance of biometric matchers based on vectors of quality measures are described. For all the three methods, the reported experimental results show significant performance improvement when applied to iris biometrics. This confirms that the newly proposed quality measures are informative in the sense that their involvement results in improved iris recognition performance.
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© 2016 Springer-Verlag London
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Schmid, N., Zuo, J., Nicolo, F., Wechsler, H. (2016). Iris Quality Metrics for Adaptive Authentication. In: Bowyer, K., Burge, M. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6784-6_5
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DOI: https://doi.org/10.1007/978-1-4471-6784-6_5
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