Abstract
Biometric recognition is often affected by low quality images. This is especially true in iris recognition fields, due to the fact that the area of the iris is quite small and wrong detection are very common when standard iris detection methods are used, like the Hough transform. In this paper, the iris quality assessment of over 1200 images is achieved, from three different datasets. The evaluation of the iris is done by using shallow learning techniques. Two different experiments have been carried out and the results obtained show good accuracy performance on the test sets.
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Acknowledgments
Mirko Marras gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014-2020, Axis III “Education and Training”, Thematic Goal 10, Priority of Investment 10ii, Specific Goal 10.5). The Italian Ministry of University, Education and Research (MIUR), partially supported this work, under the project ILEARNTV (announcement 391/2012, SMART CITIES AND COMMUNITIES AND SOCIAL INNOVATION).
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Abate, A.F., Barra, S., Casanova, A., Fenu, G., Marras, M. (2018). Iris Quality Assessment: A Statistical Approach for Biometric Security Applications. In: Castiglione, A., Pop, F., Ficco, M., Palmieri, F. (eds) Cyberspace Safety and Security. CSS 2018. Lecture Notes in Computer Science(), vol 11161. Springer, Cham. https://doi.org/10.1007/978-3-030-01689-0_21
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