Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

Patricia Melin (Tijuana Institute of Technology)
Victor Herrera (Tijuana Institute of Technology)
Danniela Romero (Tijuana Institute of Technology)
Fevrier Valdez (Tijuana Institute of Technology)
Oscar Castillo (Tijuana Institute of Technology)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 10, No 2: June 2012

This paper describes the application of modular neural network architectures for person recognition using the human iris image as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with different methods, such as: the gating network method, type-1 fuzzy integration and optimized fuzzy integration using genetic algorithms. Simulation results show a good identification rate using fuzzy integrators and the best structure found by the genetic algorithm.