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Hybrid deep convolutional neural models for iris image recognition

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Abstract

This paper briefly explains about the application of deep learning-based methods for biometric applications. This work attempts to solve the problem of limited availability of datasets which affects accuracy of the classifiers. This paper explores the iris recognition problem using a basic convolutional neural network model and hybrid deep learning models. The augmentations used to populate the dataset and their outputs are also shown in this study. An illustration of learned weights and the outputs of intermediary stages the network like convolution layer, normalization layer and activation layer are given to help better understanding of the process. The performance of the network is studied using accuracy and receiver operating characteristic curve. The empirical results of our experiments show that Adam based optimization is good at learning iris features using deep learning. Moreover, the hybrid deep learning network with SVM performs better in iris recognition with a maximum accuracy of 97.8%. These experiments have also revealed that not all hybrid networks will give better performance as the hybrid deep learning network with KNN has given lesser accuracy.

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Authors and Affiliations

  1. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    J. Jenkin Winston & D. Jude Hemanth

  2. School of Computer Science and Engineering, University of Westminster, London, UK

    Anastassia Angelopoulou & Epaminondas Kapetanios

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  1. J. Jenkin Winston
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Correspondence to D. Jude Hemanth.

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Winston, J.J., Hemanth, D.J., Angelopoulou, A. et al. Hybrid deep convolutional neural models for iris image recognition. Multimed Tools Appl 81, 9481–9503 (2022). https://doi.org/10.1007/s11042-021-11482-y

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