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Iris Center Localization Using Geodesic Distance and CNN

  • Radovan FusekEmail author
  • Eduard Sojka
Conference paper
  • 191 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

In this paper, we propose a new eye iris center localization method for remote tracking scenarios. The method combines the geodesic distance with CNN-based classification. Firstly, the geodesic distance is used for fast preliminary localization of the regions possibly containing the iris. Then a convolutional neural network is used to carry out the final decision and to refine the final position of the iris center. In the first step, the areas that do not appear to contain the eyeball are quickly filtered out, which makes the whole algorithm fast even on less powerful computers. The proposed method is evaluated and compared with the state-of-the-art methods on two publicly available datasets focused to the remote tracking scenarios (namely BioID [9], GI4E [15]).

Keywords

CNN Iris detection Geodesic distance Deep learning 

Notes

Acknowledgments

This work was partially supported by Grant of SGS No. SP2019/71, VŠB - Technical University of Ostrava, Czech Republic.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.FEECS, Department of Computer ScienceTechnical University of OstravaOstrava-PorubaCzech Republic

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