Algorithm for Iris recognition based on contourlet Transform and Entropy

Authors

  • Ayoub Ezzaki LCS laboratory, Physics Dept. Faculty of science, Mohammed V University in Rabat
  • Nadia Idrissi LCS laboratory, Physics Dept. Faculty of science, Mohammed V University in Rabat
  • Francisco-Angel Moreno Machine Perception and Intelligent Robotics Group, System Engineering and Automation Dept. and Biomedical Research Institute of Málaga (IBIMA), University of Málaga
  • Lhoussaine Masmoudi LCS laboratory, Physics Dept. Faculty of science, Mohammed V University in Rabat

Abstract

The iris is one of the most secure biometric information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Entropy which entails i) the detection and segmentation of the iris, ii) its normalization, iii) the application of the Contourlet Transform, iv) the generation of the iris descriptor, and v) the matching between the query iris and those in the database. The proposed method has been tested with images taken from the popular CASIA-V4 and UBIRIS.v1 datasets and compared against four other iris recognition algorithms. The results show a higher true positive rate with a reduced computation time.

Keywords

Iris, Biometric, Segmentation, Hough transform, contourlet Transform, entropy

DOI

https://doi.org/10.5565/rev/elcvia.1190

Published

2020-07-30

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Copyright (c) 2020 Ayoub Ezzaki, Nadia Idrissi, Francisco-Angel Moreno, Lhoussaine Masmoudi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.