In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

In this paper, I proposed an iris recognition system by using deep learning via convolutional neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code is recognizing test pictures’ category (aka person name) with high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a dataset that I added to the code. I used IITD iris dataset which included 10 iris pictures for 223 people. The pictures were divided into 3 pictures for testing and 7 pictures for training. The categories are from 001 to 223. Since the number of pictures for training is low, in order to enhance the recognition accuracy of the network, I used five sets of layers and altered nine parameters of sgdm training option. The code concluded an accuracy rate of 97.46% and the time elapsed was 10 minutes and 30 seconds.