Artificial Neural Networks for Iris Recognition System: Comparisons between Different Models, Architectures and Algorithms

摘要

In this research, an iris recognition system was suggested based on five Artificial Neural Network (ANN) models separately: feed forward (FFBPNN), cascade forward (CFBPNN), function fitting (FitNet), pattern recognition (PatternNet) and learning vector quantization (LVQNet). For each ANN model, two architectures were constructed separately; 4 layers and 7 layers, each with different numbers of hidden layer units (5, 10 and 15). Ten different ANN optimization training algorithms (LM, BFG, BR, CGF, GD, GDM, GDA, GDX, OSS and RP) were used to train each model separately. Many experiments were conducted for each one of the five models. Each model used two different architectures, a different number of hidden layer neurons and ten different training algorithms. The performance results of the models were compared according to mean square error to identify the best ANN model. The results showed that the PatternNet model was the best model used. Finally, comparisons between the ten training algorithms were performed through training the PatternNet model. Comparison results showed that TrainLM was the best training algorithm for the iris recognition system.