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Iris Quality Assessment: A Statistical Approach for Biometric Security Applications

  • Andrea F. Abate
  • Silvio BarraEmail author
  • Andrea Casanova
  • Gianni Fenu
  • Mirko Marras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11161)

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.

Keywords

Iris quality assessment Statistical approach Machine learning SVM 

Notes

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).

Source Code. In order to have the source code of the method, along with the experimental data.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly
  2. 2.Department of Computer ScienceUniversity of SalernoSalernoItaly

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