A firefly algorithm for modular granular neural networks optimization applied to iris recognition

In this paper a Modular Neural Network (MNN) with a granular approach optimization is proposed, where a firefly optimization is proposed to design a optimal MNN architecture. The proposed method can perform the optimization of some parameters such as; number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each sub module. The proposed method is applied to human recognition based on iris biometrics. A benchmark database is used to prove the efficiency and effectiveness of the proposed method, using as objective function the minimization of the error of recognition.