Hierarchical genetic optimization of modular neural networks and their type-2 fuzzy response integrators for human recognition based on multimodal biometry

In this paper we describe the application of a Modular Neural Network (MNN) for iris, ear and voice recognition for a benchmark database. The proposed MNN architecture consists of three modules; iris, ear and voice. Each module is divided into other three sub modules. Each sub module contains different information, this means one third of the database for each sub module. We considered the integration of each biometric measure separately. Later, we proceed to integrate these modules with a fuzzy integrator. Also, we performed optimization of the modular neural networks and the fuzzy integrators using genetic algorithms, and comparisons were made between optimized results and the results without optimization.