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Pupil Localization Using Self-organizing Migrating Algorithm

  • Radovan FusekEmail author
  • Petr Dobeš
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

In this paper, we propose a new method for pupil localization in images. The main contribution of the proposed method is twofold. Firstly, the method is based on the proposed eye model that takes into account physiological properties of eyes (i.e. reflects the properties of pupil, iris, and sclera). Secondly, the correct shape and the position of the model are determined using an evolutionary algorithm called Self-Organizing Migrating Algorithm (SOMA). Thanks to these ideas, the proposed method is faster than the state-of-the-art methods without reduction of accuracy. We evaluated the algorithms on two publicly available data sets in remote tracking scenarios (namely BioID [7] and GI4E [11]).

Keywords

SOMA Pupil detection Evolutionary algorithms Object detection Shape analysis 

Notes

Acknowledgments

This work was partially supported by Grant of SGS No. SP2018/42, VŠB - Technical University of Ostrava, Czech Republic.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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