Title: Enhanced iris recognition using an optimised gated recurrent unit with informative feature selection

Authors: K.R. Radhika; S.V. Sheela; P. Abhinand

Addresses: Department of Information Science and Engineering, BMS College of Engineering, Bull Temple Road, Bangalore 560019, Karnataka, India ' Department of Information Science and Engineering, BMS College of Engineering, Bull Temple Road, Bangalore 560019, Karnataka, India ' Department of Information Science and Engineering, BMS College of Engineering, Bull Temple Road, Bangalore 560019, Karnataka, India

Abstract: Iris recognition technologies are used in many applications nowadays because of the always-growing demand for identity authentication. This paper presents a strong deep learning-based system for exact iris localisation and recognition. The proposed framework consists of three steps: region segmentation, feature extraction, and recognition. The iris images were first obtained from three benchmark datasets. The multimedia university (MMU)-iris dataset, the IITD-iris dataset, and the UB-iris dataset. The interesting iris areas are then split using DIDO method in the second dimension. Thirdly, three techniques - the Harris detector, ResNet-18, and speeded up robust features (SURF) - are aggregated to extract features from the segmented iris sections. Feature extraction helps to emphasise the discriminative characteristics of Iris images clearly, therefore enabling classification models to differentiate between different patterns. These resulting discriminative features are subsequently put into the upgraded GRU model to detect matching and non-matching iris patterns. The empirical analysis revealed using the IITD-iris, MMU-iris, and UB-iris datasets that the enhanced GRU model acquired maximum recognition accuracy of 99.67%, 99.43%, and 98.78%. These achieved results surpass those of comparative models, including GRU, RNN, LSTM.

Keywords: Daugman's algorithm; University of Beira; UB; informative features security; iris recognition; Indian Institute of Technology Delhi; IITD; Daugman's Integro differential operator; DIDO; gated recurrent unit; GRU; recurrent neural network; RNN; sparse autoencoder; long short-term memory; LSTM.

DOI: 10.1504/IJICS.2025.150006

International Journal of Information and Computer Security, 2025 Vol.28 No.4, pp.445 - 473

Received: 13 Nov 2024
Accepted: 30 Apr 2025
Published online: 21 Nov 2025 *

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