Pattern Recognition Neural Network for Improving the Performance of Iris Recognition System
Omaima N. Ahmad, Abdelfatah Aref Tamimi · 2013
This research employs pattern recognition neural networks for Iris recognition systems. The neural networks have a seven- layers architecture consisting of one input layer, five hidden layers, and one output layer. Ten different ANN optimization training algorithms were used separately to train this model to get best results for the iris recognition system. Many experiments were conducted to compare the results of this model with the results of other ANN models to identify the model which improves the performance of the iris recognition system. The performances were compared using mean square error (MSE), PSNR and recognition rate to identify the best model and algorithm. The best results were obtained from the patternNet model especially when it was trained with TrainLM. The results of this model were compared also with the results of other researches to show its efficiency.