Biometric-Iris Random Key Generator Using Generalized Regression Neural Networks

  • Luis E. Garza Castañón
  • MariCarmen Pérez Reigosa
  • Juan A. Nolazco-Flores
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


In this work, we present a new approach to generate cryptographic keys from iris biometric. The main challenge of the general research is to find a suitable method to generate a cryptographic-iris-key every time the same iris information is analyzed, and this key should be different to the key generated for other users. Some problems to reach this goal are the imperfections that occurs in the biometric acquisition process, the features extraction selection and the matching algorithms. In our work, the key is calculated in four steps. First, the iris is located by use of the integrodifferential operators. Second, a set of features are computed by the use of Gabor filtering. Third, these features are divided in groups, depending on number of bits to be generated. In the final step, we generate a bit for each group of features by using a set of generalized regression neural net classifiers. We develop our experiments using a set of noisy images from the UBIRIS database, and the experimental results are very promising.


Independent Component Analysis Iris Image Back Propagation Neural Network General Regression Neural Network Iris Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis E. Garza Castañón
    • 1
  • MariCarmen Pérez Reigosa
    • 1
  • Juan A. Nolazco-Flores
    • 2
  1. 1.Department of Mechatronics and Automation
  2. 2.Computer Science DepartmentMonterreyMéxico

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