Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a
high degree of assurance. Extracting good features is the most significant step in the iris recognition
system. In the past, different features have been used to implement iris recognition system. Most of them are
depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in
computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained
much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned
features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class
Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed
system is investigated when extracting features from the segmented iris image and from the normalized iris
image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIAIris-V1,
CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the
very high accuracy rate.