Skip to main content

Iris Recognition System Using Convolutional Neural Network.

Sallam, A., Amery, H.A., Al-Qudasi, S., Al-Ghorbani, S., Rassem, T. H. and Makbol, N., 2021. Iris Recognition System Using Convolutional Neural Network. In: ICSECS-ICOCSIM 2021: International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management, 24-26 August 2021, Pekan Malaysia, 109-114.

Full text available as:

2021133080.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/ICSECS52883.2021.00027


Identification system is one of the important parts in security domains of the present time. The traditional protection methods considered to be inefficient and unreliable as they are subjected to the theft, imitation or forgetfulness. In contrast, biometrics such as facial recognition, fingerprints and the retina have emerged as modern protection methods, but still also suffer from some defects and violations. However, Iris recognition is an automated method that considered as a promising biometric identification due to the stability and the uniqueness of its patterns. In this paper, an iris recognition system based on Convolutional Neural Network (CNN) model was proposed. CNN is used to perform the required processes of feature extraction and classification. The proposed system was evaluated through CASIA-V1 and ATVS datasets, after the required pre-processing steps taken place, and achieved 98% and 97.83% as a result, respectively.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Funding Agency: 10.13039/501100005605-Universiti Malaysia Pahang 10.13039/501100002701-Ministry of Education
Uncontrolled Keywords:Location awareness , Scientific computing , Neural networks; Feature extraction; Retina; Stability analysis; Information management
Group:Faculty of Science & Technology
ID Code:37436
Deposited By: Symplectic RT2
Deposited On:05 Sep 2022 12:10
Last Modified:05 Sep 2022 12:10


Downloads per month over past year

More statistics for this item...
Repository Staff Only -