Title: Deep learning driven fusion of iris biometrics for optimised multimodal authentication informative security

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

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

Abstract: Secure authentication methods have been made possible by the high level of maturity obtained by biometric-based technologies. Artificial neural networks forecast non-parametrically using interconnected artificial neurons, like the biological nervous system. For verification, iris, hand geometry, handwriting, fingerprint, speech, retina, face, and typing rhythm were studied. Iris recognition is most popular because it accurately identifies people. This study uses iris biometric authentication. The method simulates CASIA-Thousand-Iris utilising deep convolutional neural network (DCNN) architectures EfficientNetB0.1, CNN, DenseNet, and ConvNeXt. The experiment used our retinal recognition method to accurately identify numerous retinal samples. The suggested study introduced an MSAGFF module to EfficientNetB0.1 for iris biometrics. The attention mechanism uses channel spatial attention (CSA) to reduce redundant information and improve discriminative features for accurate recognition. Adaptive fusion strategy dynamically integrates recovered features from different receptive fields to increase model durability and decision-making. For secure Iris-based identification, EfficientNetB0.1's multimodal authentication is reliable. This end-to-end strategy boosts system performance. CNN (98.79%), DenseNet (92.11%), and ConvNeXt (66.66%) had worse accuracy than EfficientNetB0.1 (99.33%). CNN architectures in biometric systems are extended by deep learning-based iris recognition for safe authentication.

Keywords: retina; convolutional neural network; CNN; informative security; multi-factor authentication; biometric identification; EfficientNetB0.1; DenseNet; ConvNeXt.

DOI: 10.1504/IJICS.2026.153343

International Journal of Information and Computer Security, 2026 Vol.29 No.4, pp.397 - 435

Received: 13 Mar 2025
Accepted: 20 Sep 2025
Published online: 01 May 2026 *

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