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Implementation of a Customized Light CNN Architecture for Iris Recognition System

International Journal of Computer Science and Engineering
© 2024 by SSRG - IJCSE Journal
Volume 11 Issue 9
Year of Publication : 2024
Authors : P. Jyothi, D. Krishna Reddy, P. Naveen Kumar
10.14445/23488387/IJCSE-V11I9P103

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How to Cite?

P. Jyothi, D. Krishna Reddy, P. Naveen Kumar, "Implementation of a Customized Light CNN Architecture for Iris Recognition System," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 9, pp. 19-25, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I9P103

Abstract:

Biometric recognition refers to a technological approach that enables the identification and authentication of individuals by leveraging their distinct physical or behavioral attributes. The utilization of this technology is prevalent in the domains of security, access control, and authentication of identity. Unlike traditional identification methods such as passwords or PINs, biometric recognition relies on the distinctive traits of an individual, making it more secure and difficult to fake. Commonly used biometric approaches include fingerprint, iris, facial, palmprint, and retina. Among them, iris recognition is used widely and has unique patterns in the colored part of the eye (iris) to identify and authenticate individuals. The iris is the ring-shaped part of the eye surrounding the pupil and is known for its distinctive and stable characteristics. Even identical twins have different iris patterns, making iris recognition highly accurate and secure. However, the security aspects of the system are still unexplored. Therefore, we proposed a convolutional neural network (CNN) architecture-based approach to identify fake iris images. The suggested model includes preprocessing, saliency detection, feature extraction, and classification by CNN. All these experiments were carried out on the CASIA-Iris-Interval and CASIA-Iris-Syn databases. Through this process, the implemented technique attained 100% accuracy.

Keywords:

Biometric recognition, Fake iris, Feature extraction, Saliency, Convolutional neural networks.

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