Convolutional Neural Network Based Feature Extraction for IRIS Recognition

International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018

14 Pages Posted: 18 Jun 2020

Date Written: 2018

Abstract

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.

Keywords: Biometrics, Iris, Recognition, Deep learning, Convolutional Neural Network (CNN), Feature extraction (FE).

Suggested Citation

Alaslani, Maram G., Convolutional Neural Network Based Feature Extraction for IRIS Recognition (2018). International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018, Available at SSRN: https://ssrn.com/abstract=3609860

Maram G. Alaslani (Contact Author)

King Abdulaziz University

Biological Sciences Department
MIT Alumini
Jeddah
Saudi Arabia

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